literature review mental health

• Research article
• Open access
• Published: 24 October 2019

A scoping review of the literature on the current mental health status of physicians and physicians-in-training in North America

• Mara Mihailescu   ORCID: orcid.org/0000-0001-6878-1024 1 &
• Elena Neiterman 2  

BMC Public Health volume  19 , Article number:  1363 ( 2019 ) Cite this article

26k Accesses

58 Citations

11 Altmetric

Metrics details

This scoping review summarizes the existing literature regarding the mental health of physicians and physicians-in-training and explores what types of mental health concerns are discussed in the literature, what is their prevalence among physicians, what are the causes of mental health concerns in physicians, what effects mental health concerns have on physicians and their patients, what interventions can be used to address them, and what are the barriers to seeking and providing care for physicians. This review aims to improve the understanding of physicians’ mental health, identify gaps in research, and propose evidence-based solutions.

A scoping review of the literature was conducted using Arksey and O’Malley’s framework, which examined peer-reviewed articles published in English during 2008–2018 with a focus on North America. Data were summarized quantitatively and thematically.

A total of 91 articles meeting eligibility criteria were reviewed. Most of the literature was specific to burnout ( n  = 69), followed by depression and suicidal ideation ( n  = 28), psychological harm and distress ( n  = 9), wellbeing and wellness ( n  = 8), and general mental health ( n  = 3). The literature had a strong focus on interventions, but had less to say about barriers for seeking help and the effects of mental health concerns among physicians on patient care.

Conclusions

More research is needed to examine a broader variety of mental health concerns in physicians and to explore barriers to seeking care. The implication of poor physician mental health on patients should also be examined more closely. Finally, the reviewed literature lacks intersectional and longitudinal studies, as well as evaluations of interventions offered to improve mental wellbeing of physicians.

Peer Review reports

The World Health Organization (WHO) defines mental health as “a state of well-being in which the individual realizes his or her own abilities, can cope with the normal stresses of life, can work productively and fruitfully, and is able to make a contribution to his or her community.” [ 41 ] One in four people worldwide are affected by mental health concerns [ 40 ]. Physicians are particularly vulnerable to experiencing mental illness due to the nature of their work, which is often stressful and characterized by shift work, irregular work hours, and a high pressure environment [ 1 , 21 , 31 ]. In North America, many physicians work in private practices with no access to formal institutional supports, which can result in higher instances of social isolation [ 13 , 27 ]. The literature on physicians’ mental health is growing, partly due to general concerns about mental wellbeing of health care workers and partly due to recognition that health care workers globally are dissatisfied with their work, which results in burnout and attrition from the workforce [ 31 , 34 ]. As a consequence, more efforts have been made globally to improve physicians’ mental health and wellness, which is known as “The Quadruple Aim.” [ 34 ] While the literature on mental health is flourishing, however, it has not been systematically summarized. This makes it challenging to identify what is being done to improve physicians’ wellbeing and which solutions are particularly promising [ 7 , 31 , 33 , 37 , 38 ]. The goal of our paper is to address this gap.

This paper explores what is known from the existing peer-reviewed literature about the mental health status of physicians and physicians-in-training in North America. Specifically, we examine (1) what types of mental health concerns among physicians are commonly discussed in the literature; (2) what are the reported causes of mental health concerns in physicians; (3) what are the effects that mental health concerns may have on physicians and their patients; (4) what solutions are proposed to improve mental health of physicians; and (5) what are the barriers to seeking and providing care to physicians with mental health concerns. Conducting this scoping review, our goal is to summarize the existing research, identifying the need for a subsequent systematic review of the literature in one or more areas under the study. We also hope to identify evidence-based interventions that can be utilized to improve physicians’ mental wellbeing and to suggest directions for future research [ 2 ]. Evidence-based interventions might have a positive impact on physicians and improve the quality of patient care they provide.

A scoping review of the academic literature on the mental health of physicians and physicians-in-training in North America was conducted using Arksey and O’Malley’s [ 2 ] methodological framework. Our review objectives and broad focus, including the general questions posed to conduct the review, lend themselves to a scoping review approach, which is suitable for the analysis of a broader range of study designs and methodologies [ 2 ]. Our goal was to map the existing research on this topic and identify knowledge gaps, without making any prior assumptions about the literature’s scope, range, and key findings [ 29 ].

Stage 1: identify the research question

Following the guidelines for scoping reviews [ 2 ], we developed a broad research question for our literature search, asking what does the academic literature tell about mental health issues among physicians, residents, and medical students in North America ? Burnout and other mental health concerns often begin in medical training and continue to worsen throughout the years of practice [ 31 ]. Recognizing that the study and practice of medicine plays a role in the emergence of mental health concerns, we focus on practicing physicians – general practitioners, specialists, and surgeons – and those who are still in training – residents and medical students. We narrowed down the focus of inquiry by asking the following sub-questions:

What types of mental health concerns among physicians are commonly discussed in the literature?

What are the reported causes of mental health problems in physicians and what solutions are available to improve the mental wellbeing of physicians?

What are the barriers to seeking and providing care to physicians suffering from mental health problems?

Stage 2: identify the relevant studies

We included in our review empirical papers published during January 2008–January 2018 in peer-reviewed journals. Our exclusive focus on peer-reviewed and empirical literature reflected our goal to develop an evidence-based platform for understanding mental health concerns in physicians. Since our focus was on prevalence of mental health concerns and promising practices available to physicians in North America, we excluded articles that were more than 10 years old, suspecting that they might be too outdated for our research interest. We also excluded papers that were not in English or outside the region of interest. Using combinations of keywords developed in consultation with a professional librarian (See Table  1 ), we searched databases PUBMed, SCOPUS, CINAHL, and PsychNET. We also screened reference lists of the papers that came up in our original search to ensure that we did not miss any relevant literature.

Stage 3: literature selection

Publications were imported into a reference manager and screened for eligibility. During initial abstract screening, 146 records were excluded for being out of scope, 75 records were excluded for being outside the region of interest, and 4 papers were excluded because they could not be retrieved. The remaining 91 papers were included into the review. Figure  1 summarizes the literature search and selection.

PRISMA Flow Diagram

Stage 4: charting the data

A literature extraction tool was created in Microsoft Excel to record the author, date of publication, location, level of training, type of article (empirical, report, commentary), and topic. Both authors coded the data inductively, first independently reading five articles and generating themes from the data, then discussing our coding and developing a coding scheme that was subsequently applied to ten more papers. We then refined and finalized the coding scheme and used it to code the rest of the data. When faced with disagreements on narrowing down the themes, we discussed our reasoning and reached consensus.

Stage 5: collating, summarizing, and reporting the results

The data was summarized by frequency and type of publication, mental health topics, and level of training. The themes inductively derived from the data included (1) description of mental health concerns affecting physicians and physicians-in-training; (2) prevalence of mental health concerns among this population; (3) possible causes that can explain the emergence of mental health concerns; (4) solutions or interventions proposed to address mental health concerns; (5) effects of mental health concerns on physicians and on patient outcomes; and (6) barriers for seeking and providing help to physicians afflicted with mental health concerns. Each paper was coded based on its relevance to major theme(s) and, if warranted, secondary focus. Therefore, one paper could have been coded in more than one category. Upon analysis, we identified the gaps in the literature.

Characteristics of included literature

The initial search yielded 316 records of which 91 publications underwent full-text review and were included in our scoping review. Our analysis revealed that the publications appear to follow a trend of increase over the course of the last decade reflecting the growing interest in physicians’ mental health. More than half of the literature was published in the last 4 years included in the review, from 2014 to 2018 ( n  = 55), with most publications in 2016 ( n  = 18) (Fig.  2 ). The majority of papers ( n  = 36) focused on practicing physicians, followed by papers on residents ( n  = 22), medical students ( n  = 21), and those discussing medical professionals with different level of training ( n  = 12). The types of publications were mostly empirical ( n  = 71), of which 46 papers were quantitative. Furthermore, the vast majority of papers focused on the United States of America (USA) ( n  = 83), with less than 9% focusing on Canada ( n  = 8). The frequency of identified themes in the literature is broken down into prevalence of mental health concerns ( n  = 15), causes of mental health concerns ( n  = 18), effects of mental health concerns on physicians and patients ( n  = 12), solutions and interventions for mental health concerns ( n  = 46), and barriers to seeking and providing care for mental health concerns ( n  = 4) (Fig.  3 ).

Number of sources by characteristics of included literature

Frequency of themes in literature ( n  = 91)

Mental health concerns and their prevalence in the literature

In this thematic category ( n  = 15), we coded the papers discussing the prevalence of specific mental health concerns among physicians and those comparing physicians’ mental health to that of the general population. Most papers focused on burnout and stress ( n  = 69), which was followed by depression and suicidal ideation ( n  = 28), psychological harm and distress ( n  = 9), wellbeing and wellness ( n  = 8), and general mental health ( n  = 3) (Fig.  4 ). The literature also identified that, on average, burnout and mental health concerns affect 30–60% of all physicians and residents [ 4 , 5 , 8 , 9 , 15 , 25 , 26 ].

Number of sources by mental health topic discussed ( n  = 91)

There was some overlap between the papers discussing burnout, depression, and suicidal ideation, suggesting that work-related stress may lead to the emergence of more serious mental health problems [ 3 , 12 , 21 ], as well as addiction and substance abuse [ 22 , 27 ]. Residency training was shown to produce the highest rates of burnout [ 4 , 8 , 19 ].

Causes of mental health concerns

Papers discussing the causes of mental health concerns in physicians formed the second largest thematic category ( n  = 18). Unbalanced schedules and increasing administrative work were defined as key factors in producing poor mental health among physicians [ 4 , 5 , 6 , 13 , 15 , 27 ]. Some papers also suggested that the nature of the medical profession itself – competitive culture and prioritizing others – can lead to the emergence of mental health concerns [ 23 , 27 ]. Indeed, focus on qualities such as rigidity, perfectionism, and excessive devotion to work during the admission into medical programs fosters the selection of students who may be particularly vulnerable to mental illness in the future [ 21 , 24 ]. The third cluster of factors affecting mental health stemmed from structural issues, such as pressure from the government and insurance, fragmentation of care, and budget cuts [ 13 , 15 , 18 ]. Work overload, lack of control over work environment, lack of balance between effort and reward, poor sense of community among staff, lack of fairness and transparency by decision makers, and dissonance between one’s personal values and work tasks are the key causes for mental health concerns among physicians [ 20 ]. Govardhan et al. conceptualized causes for mental illness as having a cyclical nature - depression leads to burnout and depersonalization, which leads to patient dissatisfaction, causing job dissatisfaction and more depression [ 19 ].

Effects of mental health concerns on physicians and patients

A relatively small proportion of papers (13%) discussed the effects of mental health concerns on physicians and patients. The literature prioritized the direct effect of mental health on physicians ( n  = 11) with only one paper focusing solely on the indirect effects physicians’ mental health may have on patients. Poor mental health in physicians was linked to decreased mental and physical health [ 3 , 14 , 15 ]. In addition, mental health concerns in physicians were associated with reduction in work hours and the number of patients seen, decrease in job satisfaction, early retirement, and problems in personal life [ 3 , 5 , 15 ]. Lu et al. found that poor mental health in physicians may result in increased medical errors and the provision of suboptimal care [ 25 ]. Thus physicians’ mental wellbeing is linked to the quality of care provided to patients [ 3 , 4 , 5 , 10 , 17 ].

Solutions and interventions

In this largest thematic category ( n  = 46) we coded the literature that offered solutions for improving mental health among physicians. We identified four major levels of interventions suggested in the literature. A sizeable proportion of literature discussed the interventions that can be broadly categorized as primary prevention of mental illness. These papers proposed to increase awareness of physicians’ mental health and to develop strategies that can help to prevent burnout from occurring in the first place [ 4 , 12 ]. Some literature also suggested programs that can help to increase resilience among physicians to withstand stress and burnout [ 9 , 20 , 27 ]. We considered the papers referring to the strategies targeting physicians currently suffering from poor mental health as tertiary prevention . This literature offered insights about mindfulness-based training and similar wellness programs that can increase self-awareness [ 16 , 18 , 27 ], as well as programs aiming to improve mental wellbeing by focusing on physical health [ 17 ].

While the aforementioned interventions target individual physicians, some literature proposed workplace/institutional interventions with primary focus on changing workplace policies and organizational culture [ 4 , 13 , 23 , 25 ]. Reducing hours spent at work and paperwork demands or developing guidelines for how long each patient is seen have been identified by some researchers as useful strategies for improving mental health [ 6 , 11 , 17 ]. Offering access to mental health services outside of one’s place of employment or training could reduce the fear of stigmatization at the workplace [ 5 , 12 ]. The proposals for cultural shift in medicine were mainly focused on promoting a less competitive culture, changing power dynamics between physicians and physicians-in-training, and improving wellbeing among medical students and residents. The literature also proposed that the medical profession needs to put more emphasis on supporting trainees, eliminating harassment, and building strong leadership [ 23 ]. Changing curriculum for medical students was considered a necessary step for the cultural shift [ 20 ]. Finally, while we only reviewed one paper that directly dealt with the governmental level of prevention, we felt that it necessitated its own sub-thematic category because it identified the link between government policy, such as health care reforms and budget cuts, and the services and care physicians can provide to their patients [ 13 ].

Barriers to seeking and providing care

Only four papers were summarized in this thematic category that explored what the literature says about barriers for seeking and providing care for physicians suffering from mental health concerns. Based on our analysis, we identified two levels of factors that can impact access to mental health care among physicians and physicians-in-training.

Individual level barriers stem from intrinsic barriers that individual physicians may experience, such as minimizing the illness [ 21 ], refusing to seek help or take part in wellness programs [ 14 ], and promoting the culture of stoicism [ 27 ] among physicians. Another barrier is stigma associated with having a mental illness. Although stigma might be experienced personally, literature suggests that acknowledging the existence of mental health concerns may have negative consequences for physicians, including loss of medical license, hospital privileges, or professional advancement [ 10 , 21 , 27 ].

Structural barriers refer to the lack of formal support for mental wellbeing [ 3 ], poor access to counselling [ 6 ], lack of promotion of available wellness programs [ 10 ], and cost of treatment. Lack of research that tests the efficacy of programs and interventions aiming to improve mental health of physicians makes it challenging to develop evidence-based programs that can be implemented at a wider scale [ 5 , 11 , 12 , 18 , 20 ].

Our analysis of the existing literature on mental health concerns in physicians and physicians-in-training in North America generated five thematic categories. Over half of the reviewed papers focused on proposing solutions, but only a few described programs that were empirically tested and proven to work. Less common were papers discussing causes for deterioration of mental health in physicians (20%) and prevalence of mental illness (16%). The literature on the effects of mental health concerns on physicians and patients (13%) focused predominantly on physicians with only a few linking physicians’ poor mental health to medical errors and decreased patient satisfaction [ 3 , 4 , 16 , 24 ]. We found that the focus on barriers for seeking and receiving help for mental health concerns (4%) was least prevalent. The topic of burnout dominated the literature (76%). It seems that the nature of physicians’ work fosters the environment that causes poor mental health [ 1 , 21 , 31 ].

While emphasis on burnout is certainly warranted, it might take away the attention paid to other mental health concerns that carry more stigma, such as depression or anxiety. Establishing a more explicit focus on other mental health concerns might promote awareness of these problems in physicians and reduce the fear such diagnosis may have for doctors’ job security [ 10 ]. On the other hand, utilizing the popularity and non-stigmatizing image of “burnout” might be instrumental in developing interventions promoting mental wellbeing among a broad range of physicians and physicians-in-training.

Table  2 summarizes the key findings from the reviewed literature that are important for our understanding of physician mental health. In order to explicitly summarize the gaps in the literature, we mapped them alongside the areas that have been relatively well studied. We found that although non-empirical papers discussed physicians’ mental wellbeing broadly, most empirical papers focused on medical specialty (e.g. neurosurgeons, family medicine, etc.) [ 4 , 8 , 15 , 19 , 25 , 28 , 35 , 36 ]. Exclusive focus on professional specialty is justified if it features a unique context for generation of mental health concerns, but it limits the ability to generalize the findings to a broader population of physicians. Also, while some papers examined the impact of gender on mental health [ 7 , 32 , 39 ], only one paper considered ethnicity as a potential factor for mental health concerns and found no association [ 4 ]. Given that mental health in the general population varies by gender, ethnicity, age, and sexual orientation, it would be prudent to examine mental health among physicians using an intersectional analysis [ 30 , 32 , 39 ]. Finally, of the empirical studies we reviewed, all but one had a cross-sectional design. Longitudinal design might offer a better understanding of the emergence and development of mental health concerns in physicians and tailor interventions to different stages of professional career. Additionally, it could provide an opportunity to evaluate programs’ and policies’ effectiveness in improving physicians’ mental health. This would also help to address the gap that we identified in the literature – an overarching focus on proposing solutions with little demonstrated evidence they actually work.

This review has several limitations. First, our focus on academic literature may have resulted in overlooking the papers that are not peer-reviewed but may provide interesting solutions to physician mental health concerns. It is possible that grey literature – reports and analyses published by government and professional organizations – offers possible solutions that we did not include in our analysis or offers a different view on physicians’ mental health. Additionally, older papers and papers not published in English may have information or interesting solutions that we did not include in our review. Second, although our findings suggest that the theme of burnout dominated the literature, this may be the result of the search criteria we employed. Third, following the scoping review methodology [ 2 ], we did not assess the quality of the papers, focusing instead on the overview of the literature. Finally, our research was restricted to North America, specifically Canada and the USA. We excluded Mexico because we believed that compared to the context of medical practice in Canada and the USA, which have some similarities, the work experiences of Mexican physicians might be different and the proposed solutions might not be readily applicable to the context of practice in Canada and the USA. However, it is important to note that differences in organization of medical practice in Canada and the USA do exist, as do differences across and within provinces in Canada and the USA. A comparative analysis can shed light on how the structure and organization of medical practice shapes the emergence of mental health concerns.

The scoping review we conducted contributes to the existing research on mental wellbeing of American and Canadian physicians by summarizing key knowledge areas and identifying key gaps and directions for future research. While the papers reviewed in our analysis focused on North America, we believe that they might be applicable to the global medical workforce. Identifying key gaps in our knowledge, we are calling for further research on these topics, including examination of medical training curricula and its impact on mental wellbeing of medical students and residents, research on common mental health concerns such as depression or anxiety, studies utilizing intersectional and longitudinal approaches, and program evaluations assessing the effectiveness of interventions aiming to improve mental wellbeing of physicians. Focus on the effect physicians’ mental health may have on the quality of care provided to patients might facilitate support from government and policy makers. We believe that large-scale interventions that are proven to work effectively can utilize an upstream approach for improving the mental health of physicians and physicians-in-training.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Preferred Reporting Items for Systematic Reviews and Meta-Analyses

United States of America

World Health Organization

Ahmed N, Devitt KS, Keshet I, Spicer J, Imrie K, Feldman L, et al. A systematic review of the effects of resident duty hour restrictions in surgery: impact on resident wellness, training, and patient outcomes. Ann Surg. 2014;259(6):1041–53.

Article   Google Scholar  

Arksey H, O’Malley L. Scoping studies: towards a methodological framework. Int J Soc Res Methodol. 2005;8(1):19–32.

Atallah F, McCalla S, Karakash S, Minkoff H. Please put on your own oxygen mask before assisting others: a call to arms to battle burnout. Am J Obstet Gynecol. 2016;215(6):731.e1.

Baer TE, Feraco AM, Tuysuzoglu Sagalowsky S, Williams D, Litman HJ, Vinci RJ. Pediatric resident burnout and attitudes toward patients. Pediatrics. 2017;139(3):e20162163. https://doi.org/10.1542/peds.2016-2163 .

Article   PubMed   Google Scholar  

Blais R, Safianyk C, Magnan A, Lapierre A. Physician, heal thyself: survey of users of the Quebec physicians health program. Can Fam Physician. 2010;56(10):e383–9.

PubMed   PubMed Central   Google Scholar  

Brennan J, McGrady A. Designing and implementing a resiliency program for family medicine residents. Int J Psychiatry Med. 2015;50(1):104–14.

Cass I, Duska LR, Blank SV, Cheng G, NC dP, Frederick PJ, et al. Stress and burnout among gynecologic oncologists: a Society of Gynecologic Oncology Evidence-based Review and Recommendations. Gynecol Oncol. 2016;143(2):421–7.

Chan AM, Cuevas ST, Jenkins J 2nd. Burnout among osteopathic residents: a cross-sectional analysis. J Am Osteopath Assoc. 2016;116(2):100–5.

Chaukos D, Chad-Friedman E, Mehta DH, Byerly L, Celik A, McCoy TH Jr, et al. Risk and resilience factors associated with resident burnout. Acad Psychiatry. 2017;41(2):189–94.

Compton MT, Frank E. Mental health concerns among Canadian physicians: results from the 2007-2008 Canadian physician health study. Compr Psychiatry. 2011;52(5):542–7.

Cunningham C, Preventing MD. Burnout. Trustee. 2016;69(2):6–7 1.

PubMed   Google Scholar  

Daskivich TJ, Jardine DA, Tseng J, Correa R, Stagg BC, Jacob KM, et al. Promotion of wellness and mental health awareness among physicians in training: perspective of a national, multispecialty panel of residents and fellows. J Grad Med Educ. 2015;7(1):143–7.

Dyrbye LN, Shanafelt TD. Physician burnout: a potential threat to successful health care reform. JAMA. 2011;305(19):2009–10.

Article   CAS   Google Scholar  

Epstein RM, Krasner MS. Physician resilience: what it means, why it matters, and how to promote it. Acad Med. 2013;88(3):301–3.

Evans RW, Ghosh K. A survey of headache medicine specialists on career satisfaction and burnout. Headache. 2015;55(10):1448–57.

Fahrenkopf AM, Sectish TC, Barger LK, Sharek PJ, Lewin D, Chiang VW, et al. Rates of medication errors among depressed and burnt out residents: prospective cohort study. BMJ. 2008;336(7642):488–91.

Fargen KM, Spiotta AM, Turner RD, Patel S. The importance of exercise in the well-rounded physician: dialogue for the inclusion of a physical fitness program in neurosurgery resident training. World Neurosurg. 2016;90:380–4.

Gabel S. Demoralization in Health Professional Practice: Development, Amelioration, and Implications for Continuing Education. J Contin Educ Health Prof 2013 Spring. 2013;33(2):118–26.

Google Scholar  

Govardhan LM, Pinelli V, Schnatz PF. Burnout, depression and job satisfaction in obstetrics and gynecology residents. Conn Med. 2012;76(7):389–95.

Jennings ML, Slavin SJ. Resident wellness matters: optimizing resident education and wellness through the learning environment. Acad Med. 2015;90(9):1246–50.

Keller EJ. Philosophy in medical education: a means of protecting mental health. Acad Psychiatry. 2014;38(4):409–13.

Krall EJ, Niazi SK, Miller MM. The status of physician health programs in Wisconsin and north central states: a look at statewide and health systems programs. WMJ. 2012;111(5):220–7.

Lemaire JB, Wallace JE. Burnout among doctors. BMJ. 2017;358:j3360.

Linzer M, Bitton A, Tu SP, Plews-Ogan M, Horowitz KR, Schwartz MD, et al. The end of the 15-20 minute primary care visit. J Gen Intern Med. 2015;30(11):1584–6.

Lu DW, Dresden S, McCloskey C, Branzetti J, Gisondi MA. Impact of burnout on self-reported patient care among emergency physicians. West J Emerg Med. 2015;16(7):996–1001.

Maslach C, Schaufeli WB, Leiter MP. Job burnout. Annu Rev Psychol. 2001;52:397–422.

McClafferty H, Brown OW. Section on integrative medicine, committee on practice and ambulatory medicine, section on integrative medicine. Physician health and wellness. Pediatrics. 2014;134(4):830–5.

Miyasaki JM, Rheaume C, Gulya L, Ellenstein A, Schwarz HB, Vidic TR, et al. Qualitative study of burnout, career satisfaction, and well-being among US neurologists in 2016. Neurology. 2017;89(16):1730–8.

Peterson J, Pearce P, Ferguson LA, Langford C. Understanding scoping reviews: definition, purpose, and process. JAANP. 2016;29:12–6.

Przedworski JM, Dovidio JF, Hardeman RR, Phelan SM, Burke SE, Ruben MA, et al. A comparison of the mental health and well-being of sexual minority and heterosexual first-year medical students: a report from the medical student CHANGE study. Acad Med. 2015;90(5):652–9.

Ripp JA, Privitera MR, West CP, Leiter R, Logio L, Shapiro J, et al. Well-being in graduate medical education: a call for action. Acad Med. 2017;92(7):914–7.

Salles A, Mueller CM, Cohen GL. Exploring the relationship between stereotype perception and Residents’ well-being. J Am Coll Surg. 2016;222(1):52–8.

Shiralkar MT, Harris TB, Eddins-Folensbee FF, Coverdale JH. A systematic review of stress-management programs for medical students. Acad Psychiatry. 2013;37(3):158–64.

Sikka R, Morath J, Leape L. The quadruple aim: care, health, cost and meaning in work. BMJ Qual Saf. 2015;24(10):608–10. https://doi.org/10.1136/bmjqs-2015-004160 .

Tawfik DS, Phibbs CS, Sexton JB, Kan P, Sharek PJ, Nisbet CC, et al. Factors Associated With Provider Burnout in the NICU. Pediatrics. 2017;139(5):608. https://doi.org/10.1542/peds.2016-4134 Epub 2017 Apr 18.

Turner TB, Dilley SE, Smith HJ, Huh WK, Modesitt SC, Rose SL, et al. The impact of physician burnout on clinical and academic productivity of gynecologic oncologists: a decision analysis. Gynecol Oncol. 2017;146(3):642–6.

West CP, Dyrbye LN, Erwin PJ, Shanafelt TD. Interventions to prevent and reduce physician burnout: a systematic review and meta-analysis. Lancet. 2016;388(10057):2272.

Williams D, Tricomi G, Gupta J, Janise A. Efficacy of burnout interventions in the medical education pipeline. Acad Psychiatry. 2015;39(1):47–54.

Woodside JR, Miller MN, Floyd MR, McGowen KR, Pfortmiller DT. Observations on burnout in family medicine and psychiatry residents. Acad Psychiatry. 2008;32(1):13–9.

World Health Organization. (2001). Mental disorders affect one in four people.

World Health Organization. Promoting mental health: concepts, emerging evidence, practice (Summary Report). Geneva: World Health Organization; 2004.

Download references

Acknowledgements

Not Applicable.

Not Applicable

Author information

Authors and affiliations.

Telfer School of Management, University of Ottawa, 55 Laurier Ave E, Ottawa, ON, K1N 6N5, Canada

Mara Mihailescu

School of Public Health and Health Systems, University of Waterloo, 200 University Avenue West, Waterloo, ON, N2L 3G1, Canada

Elena Neiterman

You can also search for this author in PubMed   Google Scholar

Contributions

M.M. and E.N. were involved in identifying the relevant research question and developing the combinations of keywords used in consultation with a professional librarian. M.M. performed the literature selection and screening of references for eligibility. Both authors were involved in the creation of the literature extraction tool in Excel. Both authors coded the data inductively, first independently reading five articles and generating themes from the data, then discussing their coding and developing a coding scheme that was subsequently applied to ten more papers. Both authors then refined and finalized the coding scheme and M.M. used it to code the rest of the data. M.M. conceptualized and wrote the first copy of the manuscript, followed by extensive drafting by both authors. E.N. was a contributor to writing the final manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Mara Mihailescu .

Ethics declarations

Ethics approval and consent to participate, consent for publication, competing interests.

The authors declare that they have no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/ ), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated.

Reprints and permissions

About this article

Cite this article.

Mihailescu, M., Neiterman, E. A scoping review of the literature on the current mental health status of physicians and physicians-in-training in North America. BMC Public Health 19 , 1363 (2019). https://doi.org/10.1186/s12889-019-7661-9

Download citation

Received : 29 April 2019

Accepted : 20 September 2019

Published : 24 October 2019

DOI : https://doi.org/10.1186/s12889-019-7661-9

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Mental health
• Mental illness
• Medical students
• Scoping review
• Interventions
• North America

BMC Public Health

ISSN: 1471-2458

• Submission enquiries: [email protected]
• General enquiries: [email protected]

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• My Account Login
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Review Article
• Open access
• Published: 22 April 2020

Deep learning in mental health outcome research: a scoping review

• Chang Su 1 ,
• Zhenxing Xu 1 ,
• Jyotishman Pathak 1 &
• Fei Wang 1  

Translational Psychiatry volume  10 , Article number:  116 ( 2020 ) Cite this article

49k Accesses

139 Citations

20 Altmetric

Metrics details

• Psychiatric disorders

Mental illnesses, such as depression, are highly prevalent and have been shown to impact an individual’s physical health. Recently, artificial intelligence (AI) methods have been introduced to assist mental health providers, including psychiatrists and psychologists, for decision-making based on patients’ historical data (e.g., medical records, behavioral data, social media usage, etc.). Deep learning (DL), as one of the most recent generation of AI technologies, has demonstrated superior performance in many real-world applications ranging from computer vision to healthcare. The goal of this study is to review existing research on applications of DL algorithms in mental health outcome research. Specifically, we first briefly overview the state-of-the-art DL techniques. Then we review the literature relevant to DL applications in mental health outcomes. According to the application scenarios, we categorize these relevant articles into four groups: diagnosis and prognosis based on clinical data, analysis of genetics and genomics data for understanding mental health conditions, vocal and visual expression data analysis for disease detection, and estimation of risk of mental illness using social media data. Finally, we discuss challenges in using DL algorithms to improve our understanding of mental health conditions and suggest several promising directions for their applications in improving mental health diagnosis and treatment.

Similar content being viewed by others

Major depressive disorder: hypothesis, mechanism, prevention and treatment

Adults who microdose psychedelics report health related motivations and lower levels of anxiety and depression compared to non-microdosers

The serotonin theory of depression: a systematic umbrella review of the evidence

Introduction.

Mental illness is a type of health condition that changes a person’s mind, emotions, or behavior (or all three), and has been shown to impact an individual’s physical health 1 , 2 . Mental health issues including depression, schizophrenia, attention-deficit hyperactivity disorder (ADHD), and autism spectrum disorder (ASD), etc., are highly prevalent today and it is estimated that around 450 million people worldwide suffer from such problems 1 . In addition to adults, children and adolescents under the age of 18 years also face the risk of mental health disorders. Moreover, mental health illnesses have also been one of the most serious and prevalent public health problems. For example, depression is a leading cause of disability and can lead to an increased risk for suicidal ideation and suicide attempts 2 .

To better understand the mental health conditions and provide better patient care, early detection of mental health problems is an essential step. Different from the diagnosis of other chronic conditions that rely on laboratory tests and measurements, mental illnesses are typically diagnosed based on an individual’s self-report to specific questionnaires designed for the detection of specific patterns of feelings or social interactions 3 . Due to the increasing availability of data pertaining to an individual’s mental health status, artificial intelligence (AI) and machine learning (ML) technologies are being applied to improve our understanding of mental health conditions and have been engaged to assist mental health providers for improved clinical decision-making 4 , 5 , 6 . As one of the latest advances in AI and ML, deep learning (DL), which transforms the data through layers of nonlinear computational processing units, provides a new paradigm to effectively gain knowledge from complex data 7 . In recent years, DL algorithms have demonstrated superior performance in many data-rich application scenarios, including healthcare 8 , 9 , 10 .

In a previous study, Shatte et al. 11 explored the application of ML techniques in mental health. They reviewed literature by grouping them into four main application domains: diagnosis, prognosis, and treatment, public health, as well as research and clinical administration. In another study, Durstewitz et al. 9 explored the emerging area of application of DL techniques in psychiatry. They focused on DL in the studies of brain dynamics and subjects’ behaviors, and presented the insights of embedding the interpretable computational models into statistical context. In contrast, this study aims to provide a scoping review of the existing research applying DL methodologies on the analysis of different types of data related to mental health conditions. The reviewed articles are organized into four main groups according to the type of the data analyzed, including the following: (1) clinical data, (2) genetic and genomics data, (3) vocal and visual expression data, and (4) social media data. Finally, the challenges the current studies faced with, as well as future research directions towards bridging the gap between the application of DL algorithms and patient care, are discussed.

Deep learning overview

ML aims at developing computational algorithms or statistical models that can automatically infer hidden patterns from data 12 , 13 . Recent years have witnessed an increasing number of ML models being developed to analyze healthcare data 4 . However, conventional ML approaches require a significant amount of feature engineering for optimal performance—a step that is necessary for most application scenarios to obtain good performance, which is usually resource- and time-consuming.

As the newest wave of ML and AI technologies, DL approaches aim at the development of an end-to-end mechanism that maps the input raw features directly into the outputs through a multi-layer network structure that is able to capture the hidden patterns within the data. In this section, we will review several popular DL model architectures, including deep feedforward neural network (DFNN), recurrent neural network (RNN) 14 , convolutional neural network (CNN) 15 , and autoencoder 16 . Figure 1 provides an overview of these architectures.

a Deep feedforward neural network (DFNN). It is the basic design of DL models. Commonly, a DFNN contains multiple hidden layers. b A recurrent neural network (RNN) is presented to process sequence data. To encode history information, each recurrent neuron receives the input element and the state vector of the predecessor neuron, and yields a hidden state fed to the successor neuron. For example, not only the individual information but also the dependence of the elements of the sequence x 1  → x 2  → x 3  → x 4  → x 5 is encoded by the RNN architecture. c Convolutional neural network (CNN). Between input layer (e.g., input neuroimage) and output layer, a CNN commonly contains three types of layers: the convolutional layer that is to generate feature maps by sliding convolutional kernels in the previous layer; the pooling layer is used to reduce dimensionality of previous convolutional layer; and the fully connected layer is to make prediction. For the illustrative purpose, this example only has one layer of each type; yet, a real-world CNN would have multiple convolutional and pooling layers (usually in an interpolated manner) and one fully connected layer. d Autoencoder consists of two components: the encoder, which learns to compress the input data into a latent representation layer by layer, whereas the decoder, inverse to the encoder, learns to reconstruct the data at the output layer. The learned compressed representations can be fed to the downstream predictive model.

Deep feedforward neural network

Artificial neural network (ANN) is proposed with the intention of mimicking how human brain works, where the basic element is an artificial neuron depicted in Fig. 2a . Mathematically, an artificial neuron is a nonlinear transformation unit, which takes the weighted summation of all inputs and feeds the result to an activation function, such as sigmoid, rectifier (i.e., rectified linear unit [ReLU]), or hyperbolic tangent (Fig. 2b ). An ANN is composed of multiple artificial neurons with different connection architectures. The simplest ANN architecture is the feedforward neural network (FNN), which stacks the neurons layer by layer in a feedforward manner (Fig. 1a ), where the neurons across adjacent layers are fully connected to each other. The first layer of the FNN is the input layer that each unit receives one dimension of the data vector. The last layer is the output layer that outputs the probabilities that a subject belonging to different classes (in classification). The layers between the input and output layers are the hidden layers. A DFNN usually contains multiple hidden layers. As shown in Fig. 2a , there is a weight parameter associated with each edge in the DFNN, which needs to be optimized by minimizing some training loss measured on a specific training dataset (usually through backpropagation 17 ). After the optimal set of parameters are learned, the DFNN can be used to predict the target value (e.g., class) of any testing data vectors. Therefore, a DFNN can be viewed as an end-to-end process that transforms a specific raw data vector to its target layer by layer. Compared with the traditional ML models, DFNN has shown superior performance in many data mining tasks and have been introduced to the analysis of clinical data and genetic data to predict mental health conditions. We will discuss the applications of these methods further in the Results section.

a An illustration of basic unit of neural networks, i.e., artificial neuron. Each input x i is associated with a weight w i . The weighted sum of all inputs Σ w i x i is fed to a nonlinear activation function f to generate the output y j of the j -th neuron, i.e., y j  =  f (Σ w i x i ). b Illustrations of the widely used nonlinear activation function.

Recurrent neural network

RNNs were designed to analyze sequential data such as natural language, speech, and video. Given an input sequence, the RNN processes one element of the sequence at a time by feeding to a recurrent neuron. To encode the historical information along the sequence, each recurrent neuron receives the input element at the corresponding time point and the output of the neuron at previous time stamp, and the output will also be provided to the neuron at next time stamp (this is also where the term “recurrent” comes from). An example RNN architecture is shown in Fig. 1b where the input is a sequence of words (a sentence). The recurrence link (i.e., the edge linking different neurons) enables RNN to capture the latent semantic dependencies among words and the syntax of the sentence. In recent years, different variants of RNN, such as long short-term memory (LSTM) 18 and gated recurrent unit 19 have been proposed, and the main difference among these models is how the input is mapped to the output for the recurrent neuron. RNN models have demonstrated state-of-the-art performance in various applications, especially natural language processing (NLP; e.g., machine translation and text-based classification); hence, they hold great premise in processing clinical notes and social media posts to detect mental health conditions as discussed below.

Convolutional neural network

CNN is a specific type of deep neural network originally designed for image analysis 15 , where each pixel corresponds to a specific input dimension describing the image. Similar to a DFNN, CNN also maps these input image pixels to the corresponding target (e.g., image class) through layers of nonlinear transformations. Different from DFNN, where only fully connected layers are considered, there are typically three types of layers in a CNN: a convolution–activation layer, a pooling layer, and a fully connected layer (Fig. 1c ). The convolution–activation layer first convolves the entire feature map obtained from previous layer with small two-dimensional convolution filters. The results from each convolution filter are activated through a nonlinear activation function in the same way as a DFNN. A pooling layer reduces the size of the feature map through sub-sampling. The fully connected layer is analogous to the hidden layer in a DFNN, where each neuron is connected to all neurons of the previous layer. The convolution–activation layer extracts locally invariant patterns from the feature maps. The pooling layer effectively reduces the feature dimensionality to avoid model overfitting. The fully connected layer explores the global feature interactions as in DFNNs. Different combinations of these three types of layers constitute different CNN architectures. Because of the various characteristics of images such as local self-similarity, compositionality, and translational and deformation invariance, CNN has demonstrated state-of-the-art performance in many computer vision tasks 7 . Hence, the CNN models are promising in processing clinical images and expression data (e.g., facial expression images) to detect mental health conditions. We will discuss the application of these methods in the Results section.

Autoencoder

Autoencoder is a special variant of the DFNN aimed at learning new (usually more compact) data representations that can optimally reconstruct the original data vectors 16 , 20 . An autoencoder typically consists of two components (Fig. 1d ) as follows: (1) the encoder, which learns new representations (usually with reduced dimensionality) from the input data through a multi-layer FNN; and (2) the decoder, which is exactly the reverse of the encoder, reconstructs the data in their original space from the representations derived from the encoder. The parameters in the autoencoder are learned through minimizing the reconstruction loss. Autoencoder has demonstrated the capacity of extracting meaningful features from raw data without any supervision information. In the studies of mental health outcomes, the use of autoencoder has resulted in desirable improvement in analyzing clinical and expression image data, which will be detailed in the Results section.

The processing and reporting of the results of this review were guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines 21 . To thoroughly review the literature, a two-step method was used to retrieve all the studies on relevant topics. First, we conducted a search of the computerized bibliographic databases including PubMed and Web of Science. The search strategy is detailed in Supplementary Appendix 1 . The literature search comprised articles published until April 2019. Next, a snowball technique was applied to identify additional studies. Furthermore, we manually searched other resources, including Google Scholar, and Institute of Electrical and Electronics Engineers (IEEE Xplore), to find additional relevant articles.

Figure 3 presents the study selection process. All articles were evaluated carefully and studies were excluded if: (1) the main outcome is not a mental health condition; (2) the model involved is not a DL algorithm; (3) full-text of the article is not accessible; and (4) the article is written not in English.

In total, 57 studies, in terms of clinical data analysis, genetic data analysis, vocal and visual expression data analysis, and social media data analysis, which met our eligibility criteria, were included in this review.

A total of 57 articles met our eligibility criteria. Most of the reviewed articles were published between 2014 and 2019. To clearly summarize these articles, we grouped them into four categories according to the types of data analyzed, including (1) clinical data, (2) genetic and genomics data, (3) vocal and visual expression data, and (4) social media data. Table 1 summarizes the characteristics of these selected studies.

Clinical data

Neuroimages.

Previous studies have shown that neuroimages can record evidence of neuropsychiatric disorders 22 , 23 . Two common types of neuroimage data analyzed in mental health studies are functional magnetic resonance imaging (fMRI) and structural MRI (sMRI) data. In fMRI data, the brain activity is measured by identification of the changes associated with blood flow, based on the fact that cerebral blood flow and neuronal activation are coupled 24 . In sMRI data, the neurological aspect of a brain is described based on the structural textures, which show some information in terms of the spatial arrangements of voxel intensities in 3D. Recently, DL technologies have been demonstrated in analyzing both fMRI and sMRI data.

One application of DL in fMRI and sMRI data is the identification of ADHD 25 , 26 , 27 , 28 , 29 , 30 , 31 . To learn meaningful information from the neuroimages, CNN and deep belief network (DBN) models were used. In particular, the CNN models were mainly used to identify local spatial patterns and DBN models were to obtain a deep hierarchical representation of the neuroimages. Different patterns were discovered between ADHDs and controls in the prefrontal cortex and cingulated cortex. Also, several studies analyzed sMRIs to investigate schizophrenia 32 , 33 , 34 , 35 , 36 , where DFNN, DBN, and autoencoder were utilized. These studies reported abnormal patterns of cortical regions and cortical–striatal–cerebellar circuit in the brain of schizophrenia patients, especially in the frontal, temporal, parietal, and insular cortices, and in some subcortical regions, including the corpus callosum, putamen, and cerebellum. Moreover, the use of DL in neuroimages also targeted at addressing other mental health disorders. Geng et al. 37 proposed to use CNN and autoencoder to acquire meaningful features from the original time series of fMRI data for predicting depression. Two studies 31 , 38 integrated the fMRI and sMRI data modalities to develop predictive models for ASDs. Significant relationships between fMRI and sMRI data were observed with regard to ASD prediction.

Challenges and opportunities

The aforementioned studies have demonstrated that the use of DL techniques in analyzing neuroimages can provide evidence in terms of mental health problems, which can be translated into clinical practice and facilitate the diagnosis of mental health illness. However, multiple challenges need to be addressed to achieve this objective. First, DL architectures generally require large data samples to train the models, which may pose a difficulty in neuroimaging analysis because of the lack of such data 39 . Second, typically the imaging data lie in a high-dimensional space, e.g., even a 64 × 64 2D neuroimage can result in 4096 features. This leads to the risk of overfitting by the DL models. To address this, most existing studies reported to utilize MRI data preprocessing tools such as Statistical Parametric Mapping ( https://www.fil.ion.ucl.ac.uk/spm/ ), Data Processing Assistant for Resting-State fMRI 40 , and fMRI Preprocessing Pipeline 41 to extract useful features before feeding to the DL models. Even though an intuitive attribute of DL is the capacity to learn meaningful features from raw data, feature engineering tools are needed especially in the case of small sample size and high-dimensionality, e.g., the neuroimage analysis. The use of such tools mitigates the overfitting risk of DL models. As reported in some selected studies 28 , 31 , 35 , 37 , the DL models can benefit from feature engineering techniques and have been shown to outperform the traditional ML models in the prediction of multiple conditions such as depression, schizophrenia, and ADHD. However, such tools extract features relying on prior knowledge; hence may omit some information that is meaningful for mental outcome research but unknown yet. An alternative way is to use CNN to automatically extract information from the raw data. As reported in the previous study 10 , CNNs perform well in processing raw neuroimage data. Among the studies reviewed in this study, three 29 , 30 , 37 reported to involve CNN layers and achieved desirable performances.

Electroencephalogram data

As a low-cost, small-size, and high temporal resolution signal containing up to several hundred channels, analysis of electroencephalogram (EEG) data has gained significant attention to study brain disorders 42 . As the EEG signal is one kind of streaming data that presents a high density and continuous characteristics, it challenges traditional feature engineering-based methods to obtain sufficient information from the raw EEG data to make accurate predictions. To address this, recently the DL models have been employed to analyze raw EEG signal data.

Four articles reviewed proposed to use DL in understanding mental health conditions based on the analysis of EEG signals. Acharya et al. 43 used CNN to extract features from the input EEG signals. They found that the EEG signals from the right hemisphere of the human brain are more distinctive in terms of the detection of depression than those from the left hemisphere. The findings provided shreds of evidence that depression is associated with a hyperactive right hemisphere. Mohan et al. 44 modeled the raw EEG signals by DFNN to obtain information about the human brain waves. They found that the signals collected from the central (C3 and C4) regions are marginally higher compared with other brain regions, which can be used to distinguish the depressed and normal subjects from the brain wave signals. Zhang et al. 45 proposed a concatenated structure of deep recurrent and 3D CNN to obtain EEG features across different tasks. They reported that the DL model can capture the spectral changes of EEG hemispheric asymmetry to distinguish different mental workload effectively. Li et al. 46 presented a computer-aided detection system by extracting multiple types of information (e.g., spectral, spatial, and temporal information) to recognize mild depression based on CNN architecture. The authors found that both spectral and temporal information of EEG are crucial for prediction of depression.

EEG data are usually classified as streaming data that are continuous and are of high density. Despite the initial success in applying DL algorithms to analyze EEG data for studying multiple mental health conditions, there exist several challenges. One major challenge is that raw EEG data gathered from sensors have a certain degree of erroneous, noisy, and redundant information caused by discharged batteries, failures in sensor readings, and intermittent communication loss in wireless sensor networks 47 . This may challenge the model in extracting meaningful information from noise. Multiple preprocessing steps (e.g., data denoising, data interpolation, data transformation, and data segmentation) are necessary for dealing with the raw EEG signal before feeding to the DL models. Besides, due to the dense characteristics in the raw EEG data, analysis of the streaming data is computationally more expensive, which poses a challenge for the model architecture selection. A proper model should be designed relatively with less training parameters. This is one reason why the reviewed studies are mainly based on the CNN architecture.

Electronic health records

Electronic health records (EHRs) are systematic collections of longitudinal, patient-centered records. Patients’ EHRs consist of both structured and unstructured data: the structured data include information about a patient’s diagnosis, medications, and laboratory test results, and the unstructured data include information in clinical notes. Recently, DL models have been applied to analyze EHR data to study mental health disorders 48 .

The first and foremost issue for analyzing the structured EHR data is how to appropriately handle the longitudinal records. Traditional ML models address this by collapsing patients’ records within a certain time window into vectors, which comprised the summary of statistics of the features in different dimensions 49 . For instance, to estimate the probability of suicide deaths, Choi et al. 50 leveraged a DFNN to model the baseline characteristics. One major limitation of these studies is the omittance of temporality among the clinical events within EHRs. To overcome this issue, RNNs are more commonly used for EHR data analysis as an RNN intuitively handles time-series data. DeepCare 51 , a long short-term memory network (LSTM)-based DL model, encodes patient’s long-term health state trajectories to predict the future outcomes of depressive episodes. As the LSTM architecture appropriately captures disease progression by modeling the illness history and the medical interventions, DeepCare achieved over 15% improvement in prediction, compared with the conventional ML methods. In addition, Lin et al. 52 designed two DFNN models for the prediction of antidepressant treatment response and remission. The authors reported that the proposed DFNN can achieve an area under the receiver operating characteristic curve (AUC) of 0.823 in predicting antidepressant response.

Analyzing the unstructured clinical notes in EHRs refers to the long-standing topic of NLP. To extract meaningful knowledge from the text, conventional NLP approaches mostly define rules or regular expressions before the analysis. However, it is challenging to enumerate all possible rules or regular expressions. Due to the recent advance of DL in NLP tasks, DL models have been developed to mine clinical text data from EHRs to study mental health conditions. Geraci et al. 53 utilized term frequency-inverse document frequency to represent the clinical documents by words and developed a DFNN model to identify individuals with depression. One major limitation of such an approach is that the semantics and syntax of sentences are lost. In this context, CNN 54 and RNN 55 have shown superiority in modeling syntax for text-based prediction. In particular, CNN has been used to mine the neuropsychiatric notes for predicting psychiatric symptom severity 56 , 57 . Tran and Kavuluru 58 used an RNN to analyze the history of present illness in neuropsychiatric notes for predicting mental health conditions. The model engaged an attention mechanism 55 , which can specify the importance of the words in prediction, making the model more interpretable than their previous CNN model 56 .

Although DL has achieved promising results in EHR analysis, several challenges remain unsolved. On one hand, different from diagnosing physical health condition such as diabetes, the diagnosis of mental health conditions lacks direct quantitative tests, such as a blood chemistry test, a buccal swab, or urinalysis. Instead, the clinicians evaluate signs and symptoms through patient interviews and questionnaires during which they gather information based on patient’s self-report. Collection and deriving inferences from such data deeply relies on the experience and subjectivity of the clinician. This may account for signals buried in noise and affect the robustness of the DL model. To address this challenge, a potential way is to comprehensively integrate multimodal clinical information, including structured and unstructured EHR information, as well as neuroimaging and EEG data. Another way is to incorporate existing medical knowledge, which can guide model being trained in the right direction. For instance, the biomedical knowledge bases contain massive verified interactions between biomedical entities, e.g., diseases, genes, and drugs 59 . Incorporating such information brings in meaningful medical constraints and may help to reduce the effects of noise on model training process. On the other hand, implementing a DL model trained from one EHR system into another system is challenging, because EHR data collection and representation is rarely standardized across hospitals and clinics. To address this issue, national/international collaborative efforts such as Observational Health Data Sciences and Informatics ( https://ohdsi.org ) have developed common data models, such as OMOP, to standardize EHR data representation for conducting observational data analysis 60 .

Genetic data

Multiple studies have found that mental disorders, e.g., depression, can be associated with genetic factors 61 , 62 . Conventional statistical studies in genetics and genomics, such as genome-wide association studies, have identified many common and rare genetic variants, such as single-nucleotide polymorphisms (SNPs), associated with mental health disorders 63 , 64 . Yet, the effect of the genetic factors is small and many more have not been discovered. With the recent developments in next-generation sequencing techniques, a massive volume of high-throughput genome or exome sequencing data are being generated, enabling researchers to study patients with mental health disorders by examining all types of genetic variations across an individual’s genome. In recent years, DL 65 , 66 has been applied to identify genetic risk factors associated with mental illness, by borrowing the capacity of DL in identifying highly complex patterns in large datasets. Khan and Wang 67 integrated genetic annotations, known brain expression quantitative trait locus, and enhancer/promoter peaks to generate feature vectors of variants, and developed a DFNN, named ncDeepBrain, to prioritized non-coding variants associated with mental disorders. To further prioritize susceptibility genes, they designed another deep model, iMEGES 68 , which integrates the ncDeepBrain score, general gene scores, and disease-specific scores for estimating gene risk. Wang et al. 69 developed a novel deep architecture that combines deep Boltzmann machine architecture 70 with conditional and lateral connections derived from the gene regulatory network. The model provided insights about intermediate phenotypes and their connections to high-level phenotypes (disease traits). Laksshman et al. 71 used exome sequencing data to predict bipolar disorder outcomes of patients. They developed a CNN and used the convolution mechanism to capture correlations of the neighboring loci within the chromosome.

Although the use of genetic data in DL in studying mental health conditions shows promise, multiple challenges need to be addressed. For DL-based risk c/gene prioritization efforts, one major challenge is the limitation of labeled data. On one hand, the positive samples are limited, as known risk SNPs or genes associated with mental health conditions are limited. For example, there are about 108 risk loci that were genome-wide significant in ASD. On the other hand, the negative samples (i.e., SNPs, variants, or genes) may not be the “true” negative, as it is unclear whether they are associated with the mental illness yet. Moreover, it is also challenging to develop DL models for analyzing patient’s sequencing data for mental illness prediction, as the sequencing data are extremely high-dimensional (over five million SNPs in the human genome). More prior domain knowledge is needed to guide the DL model extracting patterns from the high-dimensional genomic space.

Vocal and visual expression data

The use of vocal (voice or speech) and visual (video or image of facial or body behaviors) expression data has gained the attention of many studies in mental health disorders. Modeling the evolution of people’s emotional states from these modalities has been used to identify mental health status. In essence, the voice data are continuous and dense signals, whereas the video data are sequences of frames, i.e., images. Conventional ML models for analyzing such types of data suffer from the sophisticated feature extraction process. Due to the recent success of applying DL in computer vision and sequence data modeling, such models have been introduced to analyze the vocal and/or visual expression data. In this work, most articles reviewed are to predict mental health disorders based on two public datasets: (i) the Chi-Mei corpus, collected by using six emotional videos to elicit facial expressions and speech responses of the subjects of bipolar disorder, unipolar depression, and healthy controls; 72 and (ii) the International Audio/Visual Emotion Recognition Challenges (AVEC) depression dataset 73 , 74 , 75 , collected within human–computer interaction scenario. The proposed models include CNNs, RNNs, autoencoders, as well as hybrid models based on the above ones. In particular, CNNs were leveraged to encode the temporal and spectral features from the voice signals 76 , 77 , 78 , 79 , 80 and static facial or physical expression features from the video frames 79 , 81 , 82 , 83 , 84 . Autoencoders were used to learn low-dimensional representations for people’s vocal 85 , 86 and visual expression 87 , 88 , and RNNs were engaged to characterize the temporal evolution of emotion based on the CNN-learned features and/or other handcraft features 76 , 81 , 84 , 85 , 86 , 87 , 88 , 89 , 90 . Few studies focused on analyzing static images using a CNN architecture to predict mental health status. Prasetio et al. 91 identified the stress types (e.g., neutral, low stress, and high stress) from facial frontal images. Their proposed CNN model outperforms the conventional ML models by 7% in terms of prediction accuracy. Jaiswal et al. 92 investigated the relationship between facial expression/gestures and neurodevelopmental conditions. They reported accuracy over 0.93 in the diagnostic prediction of ADHD and ASD by using the CNN architecture. In addition, thermal images that track persons’ breathing patterns were also fed to a deep model to estimate psychological stress level (mental overload) 93 .

From the above summary, we can observe that analyzing vocal and visual expression data can capture the pattern of subjects’ emotion evolution to predict mental health conditions. Despite the promising initial results, there remain challenges for developing DL models in this field. One major challenge is to link vocal and visual expression data with the clinical data of patients, given the difficulties involved in collecting such expression data during clinical practice. Current studies analyzed vocal and visual expression over individual datasets. Without clinical guidance, the developed prediction models have limited clinical meanings. Linking patients’ expression information with clinical variables may help to improve both the interpretability and robustness of the model. For example, Gupta et al. 94 designed a DFNN for affective prediction from audio and video modalities. The model incorporated depression severity as the parameter, linking the effects of depression on subjects’ affective expressions. Another challenge is the limitation of the samples. For example, the Chi-Mei dataset contains vocal–visual data from only 45 individuals (15 with bipolar disorder, 15 with unipolar disorder, and 15 healthy controls). Also, there is a lack of “emotion labels” for people’s vocal and visual expression. Apart from improving the datasets, an alternative way to solve this challenge is to use transfer learning, which transfers knowledge gained with one dataset (usually more general) to the target dataset. For example, some studies trained autoencoder in public emotion database such as eNTERFACE 95 to generate emotion profiles (EPs). Other studies 83 , 84 pre-trained CNN over general facial expression datasets 96 , 97 for extracting face appearance features.

Social media data

With the widespread proliferation of social media platforms, such as Twitter and Reddit, individuals are increasingly and publicly sharing information about their mood, behavior, and any ailments one might be suffering. Such social media data have been used to identify users’ mental health state (e.g., psychological stress and suicidal ideation) 6 .

In this study, the articles that used DL to analyze social media data mainly focused on stress detection 98 , 99 , 100 , 101 , depression identification 102 , 103 , 104 , 105 , 106 , and estimation of suicide risk 103 , 105 , 107 , 108 , 109 . In general, the core concept across these work is to mine the textual, and where applicable graphical, content of users’ social media posts to discover cues for mental health disorders. In this context, the RNN and CNN were largely used by the researchers. Especially, RNN usually introduces an attention mechanism to specify the importance of the input elements in the classification process 55 . This provides some interpretability for the predictive results. For example, Ive et al. 103 proposed a hierarchical RNN architecture with an attention mechanism to predict the classes of the posts (including depression, autism, suicidewatch, anxiety, etc.). The authors observed that, benefitting from the attention mechanism, the model can predict risk text efficiently and extract text elements crucial for making decisions. Coppersmith et al. 107 used LSTM to discover quantifiable signals about suicide attempts based on social media posts. The proposed model can capture contextual information between words and obtain nuances of language related to suicide.

Apart from text, users also post images on social media. The properties of the images (e.g., color theme, saturation, and brightness) provide some cues reflecting users’ mental health status. In addition, millions of interactions and relationships among users can reflect the social environment of individuals that is also a kind of risk factors for mental illness. An increasing number of studies attempted to combine these two types of information with text content for predictive modeling. For example, Lin et al. 99 leveraged the autoencoder to extract low-level and middle-level representations from texts, images, and comments based on psychological and art theories. They further extended their work with a hybrid model based on CNN by integrating post content and social interactions 101 . The results provided an implication that the social structure of the stressed users’ friends tended to be less connected than that of the users without stress.

The aforementioned studies have demonstrated that using social media data has the potential to detect users with mental health problems. However, there are multiple challenges towards the analysis of social media data. First, given that social media data are typically de-identified, there is no straightforward way to confirm the “true positives” and “true negatives” for a given mental health condition. Enabling the linkage of user’s social media data with their EHR data—with appropriate consent and privacy protection—is challenging to scale, but has been done in a few settings 110 . In addition, most of the previous studies mainly analyzed textual and image data from social media platforms, and did not consider analyzing the social network of users. In one study, Rosenquist et al. 111 reported that the symptoms of depression are highly correlated inside the circle of friends, indicating that social network analysis is likely to be a potential way to study the prevalence of mental health problems. However, comprehensively modeling text information and network structure remains challenging. In this context, graph convolutional networks 112 have been developed to address networked data mining. Moreover, although it is possible to discover online users with mental illness by social media analysis, translation of this innovation into practical applications and offer aid to users, such as providing real-time interventions, are largely needed 113 .

Discussion: findings, open issues, and future directions

Principle findings.

The purpose of this study is to investigate the current state of applications of DL techniques in studying mental health outcomes. Out of 2261 articles identified based on our search terms, 57 studies met our inclusion criteria and were reviewed. Some studies that involved DL models but did not highlight the DL algorithms’ features on analysis were excluded. From the above results, we observed that there are a growing number of studies using DL models for studying mental health outcomes. Particularly, multiple studies have developed disease risk prediction models using both clinical and non-clinical data, and have achieved promising initial results.

DL models “think to learn” like a human brain relying on their multiple layers of interconnected computing neurons. Therefore, to train a deep neural network, there are multiple parameters (i.e., weights associated links between neurons within the network) being required to learn. This is one reason why DL has achieved great success in the fields where a massive volume of data can be easily collected, such as computer vision and text mining. Yet, in the health domain, the availability of large-scale data is very limited. For most selected studies in this review, the sample sizes are under a scale of 10 4 . Data availability is even more scarce in the fields of neuroimaging, EEG, and gene expression data, as such data reside in a very high-dimensional space. This then leads to the problem of “curse of dimensionality” 114 , which challenges the optimization of the model parameters.

One potential way to address this challenge is to reduce the dimensionality of the data by feature engineering before feeding information to the DL models. On one hand, feature extraction approaches can be used to obtain different types of features from the raw data. For example, several studies reported in this review have attempted to use preprocessing tools to extract features from neuroimaging data. On the other hand, feature selection that is commonly used in conventional ML models is also an option to reduce data dimensionality. However, the feature selection approaches are not often used in the DL application scenario, as one of the intuitive attributes of DL is the capacity to learn meaningful features from “all” available data. The alternative way to address the issue of data bias is to use transfer learning where the objective is to improve learning a new task through the transfer of knowledge from a related task that has already been learned 115 . The basic idea is that data representations learned in the earlier layers are more general, whereas those learned in the latter layers are more specific to the prediction task 116 . In particular, one can first pre-train a deep neural network in a large-scale “source” dataset, then stack fully connected layers on the top of the network and fine-tune it in the small “target” dataset in a standard backpropagation manner. Usually, samples in the “source” dataset are more general (e.g., general image data), whereas those in the “target” dataset are specific to the task (e.g., medical image data). A popular example of the success of transfer learning in the health domain is the dermatologist-level classification of skin cancer 117 . The authors introduced Google’s Inception v3 CNN architecture pre-trained over 1.28 million general images and fine-tuned in the clinical image dataset. The model achieved very high-performance results of skin cancer classification in epidermal (AUC = 0.96), melanocytic (AUC = 0.96), and melanocytic–dermoscopic images (AUC = 0.94). In facial expression-based depression prediction, Zhu et al. 83 pre-trained CNN on the public face recognition dataset to model the static facial appearance, which overcomes the issue that there is no facial expression label information. Chao et al. 84 also pre-trained CNN to encode facial expression information. The transfer scheme of both of the two studies has been demonstrated to be able to improve the prediction performance.

Diagnosis and prediction issues

Unlike the diagnosis of physical conditions that can be based on lab tests, diagnoses of the mental illness typically rely on mental health professionals’ judgment and patient self-report data. As a result, such a diagnostic system may not accurately capture the psychological deficits and symptom progression to provide appropriate therapeutic interventions 118 , 119 . This issue accordingly accounts for the limitation of the prediction models to assist clinicians to make decisions. Except for several studies using the unsupervised autoencoder for learning low-dimensional representations, most studies reviewed in this study reported using supervised DL models, which need the training set containing “true” (i.e., expert provided) labels to optimize the model parameters before the model being used to predict labels of new subjects. Inevitably, the quality of the expert-provided diagnostic labels used for training sets the upper-bound for the prediction performance of the model.

One intuitive route to address this issue is to use an unsupervised learning scheme that, instead of learning to predict clinical outcomes, aims at learning compacted yet informative representations of the raw data. A typical example is the autoencoder (as shown in Fig. 1d ), which encodes the raw data into a low-dimensional space, from which the raw data can be reconstructed. Some studies reviewed have proposed to leverage autoencoder to improve our understanding of mental health outcomes. A constraint of the autoencoder is that the input data should be preprocessed to vectors, which may lead to information loss for image and sequence data. To address this, recently convolutional-autoencoder 120 and LSTM-autoencoder 121 have been developed, which integrate the convolution layers and recurrent layers with the autoencoder architecture and enable us to learn informative low-dimensional representations from the raw image data and sequence data, respectively. For instance, Baytas et al. 122 developed a variation of LSTM-autoencoder on patient EHRs and grouped Parkinson’s disease patients into meaningful subtypes. Another potential way is to predict other clinical outcomes instead of the diagnostic labels. For example, several selected studies proposed to predict symptom severity scores 56 , 57 , 77 , 82 , 84 , 87 , 89 . In addition, Du et al. 108 attempted to identify suicide-related psychiatric stressors from users’ posts on Twitter, which plays an important role in the early prevention of suicidal behaviors. Furthermore, training model to predict future outcomes such as treatment response, emotion assessments, and relapse time is also a promising future direction.

Multimodal modeling

The field of mental health is heterogeneous. On one hand, mental illness refers to a variety of disorders that affect people’s emotions and behaviors. On the other hand, though the exact causes of most mental illnesses are unknown to date, it is becoming increasingly clear that the risk factors for these diseases are multifactorial as multiple genetic, environmental, and social factors interact to influence an individual’s mental health 123 , 124 . As a result of domain heterogeneity, researchers have the chance to study the mental health problems from different perspectives, from molecular, genomic, clinical, medical imaging, physiological signal to facial, and body expressive and online behavioral. Integrative modeling of such multimodal data means comprehensively considering different aspects of the disease, thus likely obtaining deep insight into mental health. In this context, DL models have been developed for multimodal modeling. As shown in Fig. 4 , the hierarchical structure of DL makes it easily compatible with multimodal integration. In particular, one can model each modality with a specific network and combine them by the final fully connected layers, such that parameters can be jointly learned by a typical backpropagation manner. In this review, we found an increasing number of studies have attempted to use multimodal modeling. For example, Zou et al. 28 developed a multimodal model composed of two CNNs for modeling fMRI and sMRI modalities, respectively. The model achieved 69.15% accuracy in predicting ADHD, which outperformed the unimodal models (66.04% for fMRI modal-based and 65.86% for sMRI modal-based). Yang et al. 79 proposed a multimodal model to combine vocal and visual expression for depression cognition. The model results in 39% lower prediction error than the unimodal models.

One can model each modality with a specific network and combine them using the final fully-connected layers. In this way, parameters of the entire neural network can be jointly learned in a typical backpropagation manner.

Model interpretability

Due to the end-to-end design, the DL models usually appear to be “black boxes”: they take raw data (e.g., MRI images, free-text of clinical notes, and EEG signals) as input, and yield output to reach a conclusion (e.g., the risk of a mental health disorder) without clear explanations of their inner working. Although this might not be an issue in other application domains such as identifying animals from images, in health not only the model’s prediction performance but also the clues for making the decision are important. For example in the neuroimage-based depression identification, despite estimation of the probability that a patient suffers from mental health deficits, the clinicians would focus more on recognizing abnormal regions or patterns of the brain associated with the disease. This is really important for convincing the clinical experts about the actions recommended from the predictive model, as well as for guiding appropriate interventions. In addition, as discussed above, the introduction of multimodal modeling leads to an increased challenge in making the models more interpretable. Attempts have been made to open the “black box” of DL 59 , 125 , 126 , 127 . Currently, there are two general directions for interpretable modeling: one is to involve the systematic modification of the input and the measure of any resulting changes in the output, as well as in the activation of the artificial neurons in the hidden layers. Such a strategy is usually used in CNN in identifying specific regions of an image being captured by a convolutional layer 128 . Another way is to derive tools to determine the contribution of one or more features of the input data to the output. In this case, the widely used tools include Shapley Additive Explanation 129 , LIME 127 , DeepLIFT 130 , etc., which are able to assign each feature an importance score for the specific prediction task.

Connection to therapeutic interventions

According to the studies reviewed, it is now possible to detect patients with mental illness based on different types of data. Compared with the traditional ML techniques, most of the reviewed DL models reported higher prediction accuracy. The findings suggested that the DL models are likely to assist clinicians in improved diagnosis of mental health conditions. However, to associate diagnosis of a condition with evidence-based interventions and treatment, including identification of appropriate medication 131 , prediction of treatment response 52 , and estimation of relapse risk 132 still remains a challenge. Among the reviewed studies, only one 52 proposed to target at addressing these issues. Thus, further efforts are needed to link the DL techniques with the therapeutic intervention of mental illness.

Domain knowledge

Another important direction is to incorporate domain knowledge. The existing biomedical knowledge bases are invaluable sources for solving healthcare problems 133 , 134 . Incorporating domain knowledge could address the limitation of data volume, problems of data quality, as well as model generalizability. For example, the unified medical language system 135 can help to identify medical entities from the text and gene–gene interaction databases 136 could help to identify meaningful patterns from genomic profiles.

Recent years have witnessed the increasing use of DL algorithms in healthcare and medicine. In this study, we reviewed existing studies on DL applications to study mental health outcomes. All the results available in the literature reviewed in this work illustrate the applicability and promise of DL in improving the diagnosis and treatment of patients with mental health conditions. Also, this review highlights multiple existing challenges in making DL algorithms clinically actionable for routine care, as well as promising future directions in this field.

World Health Organization. The World Health Report 2001: Mental Health: New Understanding, New Hope (World Health Organization, Switzerland, 2001).

Google Scholar  

Marcus, M., Yasamy, M. T., van Ommeren, M., Chisholm, D. & Saxena, S. Depression: A Global Public Health Concern (World Federation of Mental Health, World Health Organisation, Perth, 2012).

Hamilton, M. Development of a rating scale for primary depressive illness. Br. J. Soc. Clin. Psychol. 6 , 278–296 (1967).

CAS   PubMed   Google Scholar  

Dwyer, D. B., Falkai, P. & Koutsouleris, N. Machine learning approaches for clinical psychology and psychiatry. Annu. Rev. Clin. Psychol. 14 , 91–118 (2018).

PubMed   Google Scholar  

Lovejoy, C. A., Buch, V. & Maruthappu, M. Technology and mental health: the role of artificial intelligence. Eur. Psychiatry 55 , 1–3 (2019).

Wongkoblap, A., Vadillo, M. A. & Curcin, V. Researching mental health disorders in the era of social media: systematic review. J. Med. Internet Res. 19 , e228 (2017).

PubMed   PubMed Central   Google Scholar  

LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521 , 436 (2015).

Miotto, R., Wang, F., Wang, S., Jiang, X. & Dudley, J. T. Deep learning for healthcare: review, opportunities and challenges. Brief. Bioinformatics 19 , 1236–1246 (2017).

Durstewitz, D., Koppe, G. & Meyer-Lindenberg, A. Deep neural networks in psychiatry. Mol. Psychiatry 24 , 1583–1598 (2019).

Vieira, S., Pinaya, W. H. & Mechelli, A. Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: methods and applications. Neurosci. Biobehav. Rev. 74 , 58–75 (2017).

Shatte, A. B., Hutchinson, D. M. & Teague, S. J. Machine learning in mental health: a scoping review of methods and applications. Psychol. Med. 49 , 1426–1448 (2019).

Murphy, K. P. Machine Learning: A Probabilistic Perspective (MIT Press, Cambridge, 2012).

Biship, C. M. Pattern Recognition and Machine Learning (Information Science and Statistics) (Springer-Verlag, Berlin, 2007).

Bengio, Y., Simard, P. & Frasconi, P. Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Netw. Learn. Syst. 5 , 157–166 (1994).

CAS   Google Scholar  

LeCun, Y., Bottou, L., Bengio, Y. & Haffner, P. Gradient-based learning applied to document recognition. Proc. IEEE 86 , 2278–2324 (1998).

Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y. & Manzagol, P. A. Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11 , 3371–3408 (2010).

Rumelhart, D. E., Hinton, G. E. & Williams, R. J. Learning representations by back-propagating errors. Cogn. modeling. 5 , 1 (1988).

Hochreiter, S. & Schmidhuber, J. Long short-term memory. Neural Comput. 9 , 1735–1780 (1997).

Cho, K., Van Merriënboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: encoder-decoder approaches. In Proc . SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation 103–111 (Doha, Qatar, 2014).

Liou, C., Cheng, W., Liou, J. & Liou, D. Autoencoder for words. Neurocomputing 139 , 84–96 (2014).

Moher, D., Liberati, A., Tetzlaff, J. & Altman, D. G. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Ann. Intern. Med. 151 , 264–269 (2009).

Schnack, H. G. et al. Can structural MRI aid in clinical classification? A machine learning study in two independent samples of patients with schizophrenia, bipolar disorder and healthy subjects. Neuroimage 84 , 299–306 (2014).

O’Toole, A. J. et al. Theoretical, statistical, and practical perspectives on pattern-based classification approaches to the analysis of functional neuroimaging data. J. Cogn. Neurosci. 19 , 1735–1752 (2007).

Logothetis, N. K., Pauls, J., Augath, M., Trinath, T. & Oeltermann, A. Neurophysiological investigation of the basis of the fMRI signal. Nature 412 , 150 (2001).

Kuang, D. & He, L. Classification on ADHD with deep learning. In Proc . Int. Conference on Cloud Computing and Big Data 27–32 (Wuhan, China, 2014).

Kuang, D., Guo, X., An, X., Zhao, Y. & He, L. Discrimination of ADHD based on fMRI data with deep belief network. In Proc . Int. Conference on Intelligent Computing 225–232 (Taiyuan, China, 2014).

Farzi, S., Kianian, S. & Rastkhadive, I. Diagnosis of attention deficit hyperactivity disorder using deep belief network based on greedy approach. In Proc . 5th Int. Symposium on Computational and Business Intelligence 96–99 (Dubai, United Arab Emirates, 2017).

Zou, L., Zheng, J. & McKeown, M. J. Deep learning based automatic diagnoses of attention deficit hyperactive disorder. In Proc . 2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP) 962–966 (Montreal, Canada, 2017).

Riaz A. et al. Deep fMRI: an end-to-end deep network for classification of fMRI data. In Proc . 2018 IEEE 15th Int. Symposium on Biomedical Imaging . 1419–1422 (Washington, DC, USA, 2018).

Zou, L., Zheng, J., Miao, C., Mckeown, M. J. & Wang, Z. J. 3D CNN based automatic diagnosis of attention deficit hyperactivity disorder using functional and structural MRI. IEEE Access. 5 , 23626–23636 (2017).

Sen, B., Borle, N. C., Greiner, R. & Brown, M. R. A general prediction model for the detection of ADHD and Autism using structural and functional MRI. PLoS ONE 13 , e0194856 (2018).

Zeng, L. et al. Multi-site diagnostic classification of schizophrenia using discriminant deep learning with functional connectivity MRI. EBioMedicine 30 , 74–85 (2018).

Pinaya, W. H. et al. Using deep belief network modelling to characterize differences in brain morphometry in schizophrenia. Sci. Rep. 6 , 38897 (2016).

CAS   PubMed   PubMed Central   Google Scholar  

Pinaya, W. H., Mechelli, A. & Sato, J. R. Using deep autoencoders to identify abnormal brain structural patterns in neuropsychiatric disorders: a large-scale multi-sample study. Hum. Brain Mapp. 40 , 944–954 (2019).

Ulloa, A., Plis, S., Erhardt, E. & Calhoun, V. Synthetic structural magnetic resonance image generator improves deep learning prediction of schizophrenia. In Proc . 25th IEEE Int. Workshop on Machine Learning for Signal Processing (MLSP) 1–6 (Boston, MA, USA, 2015).

Matsubara, T., Tashiro, T. & Uehara, K. Deep neural generative model of functional MRI images for psychiatric disorder diagnosis. IEEE Trans. Biomed. Eng . 99 (2019).

Geng, X. & Xu, J. Application of autoencoder in depression diagnosis. In 2017 3rd Int. Conference on Computer Science and Mechanical Automation (Wuhan, China, 2017).

Aghdam, M. A., Sharifi, A. & Pedram, M. M. Combination of rs-fMRI and sMRI data to discriminate autism spectrum disorders in young children using deep belief network. J. Digit. Imaging 31 , 895–903 (2018).

Shen, D., Wu, G. & Suk, H. -I. Deep learning in medical image analysis. Annu. Rev. Biomed. Eng. 19 , 221–248 (2017).

Yan, C. & Zang, Y. DPARSF: a MATLAB toolbox for “pipeline” data analysis of resting-state fMRI. Front. Syst. Neurosci. 4 , 13 (2010).

Esteban, O. et al. fMRIPrep: a robust preprocessing pipeline for functional MRI. Nat. Methods 16 , 111–116 (2019).

Herrmann, C. & Demiralp, T. Human EEG gamma oscillations in neuropsychiatric disorders. Clin. Neurophysiol. 116 , 2719–2733 (2005).

Acharya, U. R. et al. Automated EEG-based screening of depression using deep convolutional neural network. Comput. Meth. Prog. Biol. 161 , 103–113 (2018).

Mohan, Y., Chee, S. S., Xin, D. K. P. & Foong, L. P. Artificial neural network for classification of depressive and normal. In EEG Proc . 2016 IEEE EMBS Conference on Biomedical Engineering and Sciences 286–290 (Kuala Lumpur, Malaysia, 2016).

Zhang, P., Wang, X., Zhang, W. & Chen, J. Learning spatial–spectral–temporal EEG features with recurrent 3D convolutional neural networks for cross-task mental workload assessment. IEEE Trans. Neural Syst. Rehabil. Eng. 27 , 31–42 (2018).

Li, X. et al. EEG-based mild depression recognition using convolutional neural network. Med. Biol. Eng. Comput . 47 , 1341–1352 (2019).

Patel, S., Park, H., Bonato, P., Chan, L. & Rodgers, M. A review of wearable sensors and systems with application in rehabilitation. J. Neuroeng. Rehabil. 9 , 21 (2012).

Smoller, J. W. The use of electronic health records for psychiatric phenotyping and genomics. Am. J. Med. Genet. B Neuropsychiatr. Genet. 177 , 601–612 (2018).

Wu, J., Roy, J. & Stewart, W. F. Prediction modeling using EHR data: challenges, strategies, and a comparison of machine learning approaches. Med. Care. 48 , S106–S113 (2010).

Choi, S. B., Lee, W., Yoon, J. H., Won, J. U. & Kim, D. W. Ten-year prediction of suicide death using Cox regression and machine learning in a nationwide retrospective cohort study in South Korea. J. Affect. Disord. 231 , 8–14 (2018).

Pham, T., Tran, T., Phung, D. & Venkatesh, S. Predicting healthcare trajectories from medical records: a deep learning approach. J. Biomed. Inform. 69 , 218–229 (2017).

Lin, E. et al. A deep learning approach for predicting antidepressant response in major depression using clinical and genetic biomarkers. Front. Psychiatry 9 , 290 (2018).

Geraci, J. et al. Applying deep neural networks to unstructured text notes in electronic medical records for phenotyping youth depression. Evid. Based Ment. Health 20 , 83–87 (2017).

Kim, Y. Convolutional neural networks for sentence classification. arXiv Prepr. arXiv 1408 , 5882 (2014).

Yang, Z. et al. Hierarchical attention networks for document classification. In Proc . 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies 1480–1489 (San Diego, California, USA, 2016).

Rios, A. & Kavuluru, R. Ordinal convolutional neural networks for predicting RDoC positive valence psychiatric symptom severity scores. J. Biomed. Inform. 75 , S85–S93 (2017).

Dai, H. & Jonnagaddala, J. Assessing the severity of positive valence symptoms in initial psychiatric evaluation records: Should we use convolutional neural networks? PLoS ONE 13 , e0204493 (2018).

Tran, T. & Kavuluru, R. Predicting mental conditions based on “history of present illness” in psychiatric notes with deep neural networks. J. Biomed. Inform. 75 , S138–S148 (2017).

Samek, W., Binder, A., Montavon, G., Lapuschkin, S. & Müller, K.-R. Evaluating the visualization of what a deep neural network has learned. IEEE Trans. Neural Netw. Learn. Syst. 28 , 2660–2673 (2016).

Hripcsak, G. et al. Characterizing treatment pathways at scale using the OHDSI network. Proc. Natl. Acad. Sci . USA 113 , 7329–7336 (2016).

McGuffin, P., Owen, M. J. & Gottesman, I. I. Psychiatric Genetics and Genomics (Oxford Univ. Press, New York, 2004).

Levinson, D. F. The genetics of depression: a review. Biol. Psychiatry 60 , 84–92 (2006).

Wray, N. R. et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat. Genet. 50 , 668 (2018).

Mullins, N. & Lewis, C. M. Genetics of depression: progress at last. Curr. Psychiatry Rep. 19 , 43 (2017).

Zou, J. et al. A primer on deep learning in genomics. Nat. Genet. 51 , 12–18 (2019).

Yue, T. & Wang, H. Deep learning for genomics: a concise overview. Preprint at arXiv:1802.00810 (2018).

Khan, A. & Wang, K. A deep learning based scoring system for prioritizing susceptibility variants for mental disorders. In Proc . 2017 IEEE Int. Conference on Bioinformatics and Biomedicine (BIBM) 1698–1705 (Kansas City, USA, 2017).

Khan, A., Liu, Q. & Wang, K. iMEGES: integrated mental-disorder genome score by deep neural network for prioritizing the susceptibility genes for mental disorders in personal genomes. BMC Bioinformatics 19 , 501 (2018).

Wang, D. et al. Comprehensive functional genomic resource and integrative model for the human brain. Science 362 , eaat8464 (2018).

Salakhutdinov, R. & Hinton, G. Deep Boltzmann machines. In Proc . 12th Int. Conference on Artificial Intelligence and Statistics 448–455 (Clearwater, Florida, USA, 2009).

Laksshman, S., Bhat, R. R., Viswanath, V. & Li, X. DeepBipolar: Identifying genomic mutations for bipolar disorder via deep learning. Hum. Mutat. 38 , 1217–1224 (2017).

CAS   PubMed Central   Google Scholar  

Huang, K.-Y. et al. Data collection of elicited facial expressions and speech responses for mood disorder detection. In Proc . 2015 Int. Conference on Orange Technologies (ICOT) 42–45 (Hong Kong, China, 2015).

Valstar, M. et al. AVEC 2013: the continuous audio/visual emotion and depression recognition challenge. In Proc . 3rd ACM Int. Workshop on Audio/Visual Emotion Challenge 3–10 (Barcelona, Spain, 2013).

Valstar, M. et al. Avec 2014: 3d dimensional affect and depression recognition challenge. In Proc . 4th Int. Workshop on Audio/Visual Emotion Challenge 3–10 (Orlando, Florida, USA, 2014).

Valstar, M. et al. Avec 2016: depression, mood, and emotion recognition workshop and challenge. In Proc . 6th Int. Workshop on Audio/Visual Emotion Challenge 3–10 (Amsterdam, The Netherlands, 2016).

Ma, X., Yang, H., Chen, Q., Huang, D. & Wang, Y. Depaudionet: an efficient deep model for audio based depression classification. In Proc . 6th Int. Workshop on Audio/Visual Emotion Challenge 35–42 (Amsterdam, The Netherlands, 2016).

He, L. & Cao, C. Automated depression analysis using convolutional neural networks from speech. J. Biomed. Inform. 83 , 103–111 (2018).

Li, J., Fu, X., Shao, Z. & Shang, Y. Improvement on speech depression recognition based on deep networks. In Proc . 2018 Chinese Automation Congress (CAC) 2705–2709 (Xi’an, China, 2018).

Yang, L., Jiang, D., Han, W. & Sahli, H. DCNN and DNN based multi-modal depression recognition. In Proc . 2017 7th Int. Conference on Affective Computing and Intelligent Interaction 484–489 (San Antonio, Texas, USA, 2017).

Huang, K. Y., Wu, C. H. & Su, M. H. Attention-based convolutional neural network and long short-term memory for short-term detection of mood disorders based on elicited speech responses. Pattern Recogn. 88 , 668–678 (2019).

Dawood, A., Turner, S. & Perepa, P. Affective computational model to extract natural affective states of students with Asperger syndrome (AS) in computer-based learning environment. IEEE Access. 6 , 67026–67034 (2018).

Song, S., Shen, L. & Valstar, M. Human behaviour-based automatic depression analysis using hand-crafted statistics and deep learned spectral features. In Proc . 13th IEEE Int. Conference on Automatic Face & Gesture Recognition 158–165 (Xi’an, China, 2018).

Zhu, Y., Shang, Y., Shao, Z. & Guo, G. Automated depression diagnosis based on deep networks to encode facial appearance and dynamics. IEEE Trans. Affect. Comput. 9 , 578–584 (2018).

Chao, L., Tao, J., Yang, M. & Li, Y. Multi task sequence learning for depression scale prediction from video. In Proc . 2015 Int. Conference on Affective Computing and Intelligent Interaction (ACII) 526–531 (Xi’an, China, 2015).

Yang, T. H., Wu, C. H., Huang, K. Y. & Su, M. H. Detection of mood disorder using speech emotion profiles and LSTM. In Proc . 10th Int. Symposium on Chinese Spoken Language Processing (ISCSLP) 1–5 (Tianjin, China, 2016).

Huang, K. Y., Wu, C. H., Su, M. H. & Chou, C. H. Mood disorder identification using deep bottleneck features of elicited speech. In Proc . 2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) 1648–1652 (Kuala Lumpur, Malaysia, 2017).

Jan, A., Meng, H., Gaus, Y. F. B. A. & Zhang, F. Artificial intelligent system for automatic depression level analysis through visual and vocal expressions. IEEE Trans. Cogn. Dev. Syst. 10 , 668–680 (2017).

Su, M. H., Wu, C. H., Huang, K. Y. & Yang, T. H. Cell-coupled long short-term memory with l-skip fusion mechanism for mood disorder detection through elicited audiovisual features. IEEE Trans. Neural Netw. Learn. Syst . 31 (2019).

Harati, S., Crowell, A., Mayberg, H. & Nemati, S. Depression severity classification from speech emotion. In Proc . 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 5763–5766 (Honolulu, HI, USA, 2018).

Su, M. H., Wu, C. H., Huang, K. Y., Hong, Q. B. & Wang, H. M. Exploring microscopic fluctuation of facial expression for mood disorder classification. In Proc . 2017 Int. Conference on Orange Technologies (ICOT) 65–69 (Singapore, 2017).

Prasetio, B. H., Tamura, H. & Tanno, K. The facial stress recognition based on multi-histogram features and convolutional neural network. In Proc . 2018 IEEE Int. Conference on Systems, Man, and Cybernetics (SMC) 881–887 (Miyazaki, Japan, 2018).

Jaiswal, S., Valstar, M. F., Gillott, A. & Daley, D. Automatic detection of ADHD and ASD from expressive behaviour in RGBD data. In Proc . 12th IEEE Int. Conference on Automatic Face & Gesture Recognition 762–769 (Washington, DC, USA, 2017).

Cho, Y., Bianchi-Berthouze, N. & Julier, S. J. DeepBreath: deep learning of breathing patterns for automatic stress recognition using low-cost thermal imaging in unconstrained settings. In Proc . 2017 7th Int. Conference on Affective Computing and Intelligent Interaction (ACII) 456–463 (San Antonio, Texas, USA, 2017).

Gupta, R., Sahu, S., Espy-Wilson, C. Y. & Narayanan, S. S. An affect prediction approach through depression severity parameter incorporation in neural networks. In Proc . 2017 Int. Conference on INTERSPEECH 3122–3126 (Stockholm, Sweden, 2017).

Martin, O., Kotsia, I., Macq, B. & Pitas, I. The eNTERFACE'05 audio-visual emotion database. In Proc . 22nd Int. Conference on Data Engineering Workshops 8–8 (Atlanta, GA, USA, 2006).

Goodfellow, I. J. et al. Challenges in representation learning: A report on three machine learning contests. In Proc . Int. Conference on Neural Information Processing 117–124 (Daegu, Korea, 2013).

Yi, D., Lei, Z., Liao, S. & Li, S. Z.. Learning face representation from scratch. Preprint at arXiv 1411.7923 (2014).

Lin, H. et al. User-level psychological stress detection from social media using deep neural network. In Proc . 22nd ACM Int. Conference on Multimedia 507–516 (Orlando, Florida, USA, 2014).

Lin, H. et al. Psychological stress detection from cross-media microblog data using deep sparse neural network. In Proc . 2014 IEEE Int. Conference on Multimedia and Expo 1–6 (Chengdu, China, 2014).

Li, Q. et al. Correlating stressor events for social network based adolescent stress prediction. In Proc . Int. Conference on Database Systems for Advanced Applications 642–658 (Suzhou, China, 2017).

Lin, H. et al. Detecting stress based on social interactions in social networks. IEEE Trans. Knowl. Data En. 29 , 1820–1833 (2017).

Cong, Q. et al. X-A-BiLSTM: a deep learning approach for depression detection in imbalanced data. In Proc . 2018 IEEE Int. Conference on Bioinformatics and Biomedicine (BIBM) 1624–1627 (Madrid, Spain, 2018).

Ive, J., Gkotsis, G., Dutta, R., Stewart, R. & Velupillai, S. Hierarchical neural model with attention mechanisms for the classification of social media text related to mental health. In Proc . Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic 69–77 (New Orleans, Los Angeles, USA, 2018).

Sadeque, F., Xu, D. & Bethard, S. UArizona at the CLEF eRisk 2017 pilot task: linear and recurrent models for early depression detection. CEUR Workshop Proc . 1866 (2017).

Fraga, B. S., da Silva, A. P. C. & Murai, F. Online social networks in health care: a study of mental disorders on Reddit. In Proc . 2018 IEEE/WIC/ACM Int. Conference on Web Intelligence (WI) 568–573 (Santiago, Chile, 2018).

Gkotsis, G. et al. Characterisation of mental health conditions in social media using Informed Deep Learning. Sci. Rep. 7 , 45141 (2017).

Coppersmith, G., Leary, R., Crutchley, P. & Fine, A. Natural language processing of social media as screening for suicide risk. Biomed. Inform. Insights 10 , 1178222618792860 (2018).

Du, J. et al. Extracting psychiatric stressors for suicide from social media using deep learning. BMC Med. Inform. Decis. Mak. 18 , 43 (2018).

Alambo, A. et al. Question answering for suicide risk assessment using Reddit. In Proc . IEEE 13th Int. Conference on Semantic Computing 468–473 (Newport Beach, California, USA, 2019).

Eichstaedt, J. C. et al. Facebook language predicts depression in medical records. Proc. Natl Acad. Sci. USA 115 , 11203–11208 (2018).

Rosenquist, J. N., Fowler, J. H. & Christakis, N. A. Social network determinants of depression. Mol. Psychiatry 16 , 273 (2011).

Kipf, T. N. & Welling, M. Semi-supervised classification with graph convolutional networks. In Proc. 2017 Int. Conference on Learning Representations (Toulon, France, 2017).

Rice, S. M. et al. Online and social networking interventions for the treatment of depression in young people: a systematic review. J. Med. Internet Res. 16 , e206 (2014).

Hastie, T., Tibshirani, R. & Friedman, J. The elements of statistical learning: data mining, inference, and prediction. Springer Series in Statistics. Math. Intell. 27 , 83–85 (2009).

Torrey, L. & Shavlik, J. in Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques 242–264 (IGI Global, 2010).

Yosinski, J., Clune, J., Bengio, Y. & Lipson, H. How transferable are features in deep neural networks? In Proc . Advances in Neural Information Processing Systems 3320–3328 (Montreal, Canada, 2014).

Esteva, A. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542 , 115 (2017).

Insel, T. et al. Research domain criteria (RDoC): toward a new classification framework for research on mental disorders. Am. Psychiatr. Assoc. 167 , 748–751 (2010).

Nelson, B., McGorry, P. D., Wichers, M., Wigman, J. T. & Hartmann, J. A. Moving from static to dynamic models of the onset of mental disorder: a review. JAMA Psychiatry 74 , 528–534 (2017).

Guo, X., Liu, X., Zhu, E. & Yin, J. Deep clustering with convolutional autoencoders. In Proc . Int. Conference on Neural Information Processing 373–382 (Guangzhou, China, 2017).

Srivastava, N., Mansimov, E. & Salakhudinov, R. Unsupervised learning of video representations using LSTMs. In Proc . Int. Conference on Machine Learning 843–852 (Lille, France, 2015).

Baytas, I. M. et al. Patient subtyping via time-aware LSTM networks. In Proc . 23rd ACM SIGKDD Int. Conference on Knowledge Discovery and Data Mining 65–74 (Halifax, Canada, 2017).

American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders (DSM-5®) (American Psychiatric Pub, Washington, DC, 2013).

Biological Sciences Curriculum Study. In: NIH Curriculum Supplement Series (Internet) (National Institutes of Health, USA, 2007).

Noh, H., Hong, S. & Han, B. Learning deconvolution network for semantic segmentation. In Proc . IEEE Int. Conference on Computer Vision 1520–1528 (Santiago, Chile, 2015).

Grün, F., Rupprecht, C., Navab, N. & Tombari, F. A taxonomy and library for visualizing learned features in convolutional neural networks. In Proc. 33rd Int. Conference on Machine Learning (ICML) Workshop on Visualization for Deep Learning (New York, USA, 2016).

Ribeiro, M. T., Singh, S. & Guestrin, C. Why should I trust you?: Explaining the predictions of any classifier. In Proc . 22nd ACM SIGKDD Int. Conference on Knowledge Discovery and Data Mining 1135–1144 (San Francisco, CA, 2016).

Zhang, Q. S. & Zhu, S. C. Visual interpretability for deep learning: a survey. Front. Inf. Technol. Electron. Eng. 19 , 27–39 (2018).

Lundberg, S. M. & Lee, S. I. A unified approach to interpreting model predictions. In Proc . 31st Conference on Neural Information Processing Systems 4765–4774 (Long Beach, CA, 2017).

Shrikumar, A., Greenside, P., Shcherbina, A. & Kundaje, A. Not just a black box: learning important features through propagating activation differences. In Proc . 33rd Int. Conference on Machine Learning (New York, NY, 2016).

Gawehn, E., Hiss, J. A. & Schneider, G. Deep learning in drug discovery. Mol. Inform. 35 , 3–14 (2016).

Jerez-Aragonés, J. M., Gómez-Ruiz, J. A., Ramos-Jiménez, G., Muñoz-Pérez, J. & Alba-Conejo, E. A combined neural network and decision trees model for prognosis of breast cancer relapse. Artif. Intell. Med. 27 , 45–63 (2003).

Zhu, Y., Elemento, O., Pathak, J. & Wang, F. Drug knowledge bases and their applications in biomedical informatics research. Brief. Bioinformatics 20 , 1308–1321 (2018).

Su, C., Tong, J., Zhu, Y., Cui, P. & Wang, F. Network embedding in biomedical data science. Brief. Bioinform . https://doi.org/10.1093/bib/bby117 (2018).

Bodenreider, O. The unified medical language system (UMLS): integrating biomedical terminology. Nucleic Acids Res. 32 (suppl_1), D267–D270 (2004).

Szklarczyk, D. et al. STRING v10: protein–protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 43 , D447–D452 (2014).

Download references

Acknowledgements

The work is supported by NSF 1750326, R01 MH112148, R01 MH105384, R01 MH119177, R01 MH121922, and P50 MH113838.

Author information

Authors and affiliations.

Department of Healthcare Policy and Research, Weill Cornell Medicine, New York, NY, USA

Chang Su, Zhenxing Xu, Jyotishman Pathak & Fei Wang

You can also search for this author in PubMed   Google Scholar

Contributions

C.S., Z.X. and F.W. planned and structured the whole paper. C.S. and Z.X. conducted the literature review and drafted the manuscript. J.P. and F.W. reviewed and edited the manuscript.

Corresponding author

Correspondence to Fei Wang .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplemental material, rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Su, C., Xu, Z., Pathak, J. et al. Deep learning in mental health outcome research: a scoping review. Transl Psychiatry 10 , 116 (2020). https://doi.org/10.1038/s41398-020-0780-3

Download citation

Received : 31 August 2019

Revised : 17 February 2020

Accepted : 26 February 2020

Published : 22 April 2020

DOI : https://doi.org/10.1038/s41398-020-0780-3

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

This article is cited by

Automated mood disorder symptoms monitoring from multivariate time-series sensory data: getting the full picture beyond a single number.

• Filippo Corponi
• Bryan M. Li
• Antonio Vergari

Translational Psychiatry (2024)

Detecting your depression with your smartphone? – An ethical analysis of epistemic injustice in passive self-tracking apps

• Mirjam Faissner
• Sebastian Laacke

Ethics and Information Technology (2024)

Development of intelligent system based on synthesis of affective signals and deep neural networks to foster mental health of the Indian virtual community

• Mandeep Kaur Arora
• Jaspreet Singh

Social Network Analysis and Mining (2024)

Prevalence and predictors of self-rated mental health among farm and non-farm adult rural residents of Saskatchewan

• Md Saiful Alam
• Bonnie Janzen
• Punam Pahwa

Current Psychology (2024)

Unraveling minds in the digital era: a review on mapping mental health disorders through machine learning techniques using online social media

Quick links.

• Explore articles by subject
• Guide to authors
• Editorial policies

SYSTEMATIC REVIEW article

Promoting university students' mental health: a systematic literature review introducing the 4m-model of individual-level interventions.

• 1 Guidance & Counseling Office, Student Services & Registration, Mohammed Bin Rashid University of Medicine and Health Sciences (MBRU), Dubai, United Arab Emirates
• 2 Strategy & Institutional Excellence, Mohammed Bin Rashid University of Medicine and Health Sciences (MBRU), Dubai, United Arab Emirates

Objective: The purpose of this study is to systematically review recently published individual student-level interventions aimed at alleviating the burden of mental health challenges faced by the students and/ or at equipping them with coping mechanism that will foster their resilience.

Methods: This study relied on a systematic literature review. PubMed dataset was used; the search was confined to the following period: July 2016-December 2020.

Results: A total of 1,399 records were identified by the electronic search, out of which 40 studies were included in this study. The authors inductively identified four overlapping categories of interventions across all included articles, and coded them as follows: Mindfulness, Movement, Meaning, and Moderator. Accordingly, each study was linked to at least one of four overlapping categories based on the nature of the intervention(s) under investigation, leading to differing assortments of categories.

Conclusions: The 4M-Model generated by this study encourages focusing on devising holistic, university-based interventions that embrace the individuality of students to improve their mental health through elements of mindfulness, movement, meaning, and moderator. Through this focused approach, university counselors are enabled to design interventions that address students' physical, psychological, emotional, and social needs.

Introduction

There has been a positive paradigm shift in the way our world and its citizens are perceiving the concept of mental health. Mental health is a state of well-being that allows individuals to enjoy and maintain relationships as well as handle stress in a healthy manner without compromising on productivity ( 1 ).

A large body of literature on tertiary education students highlights the importance of maintaining mental health with evidence relating it to educational attainment and productivity ( 2 ), social relationships, engagement on campus, and quality of life ( 3 ), and placement performance ( 4 ). Poor mental health has also been linked with lower retention within a programme, grade point averages, and graduation rates among university students ( 5 ). Counseling, psychoeducation, and mental health services on campuses are no longer deemed as merely supportive but rather an integral component necessary to empower students. These services are integral to help students develop skills such as psychological flexibility ( 6 ) which in turn influences mental health ( 1 ).

The current generation of university students is vastly different from previous generations, especially in their attitudes and beliefs toward their mental health needs. Well-being is a dynamic concept of interlinked physical, social, and psychological dimensions which is constantly changing depending on intrinsic and extrinsic environments and motivations ( 7 ). It is not only the demographics of the current generation of university students that has changed considerably from the past ( 8 ), but so have their attitudes and beliefs toward their needs, including mental health ( 3 ). This population is considered high risk because most mental health problems are triggered before the age of 24 ( 9 ). There is enough evidence to link personal and academic stressors to mental health ( 10 – 12 ). Contemporary tertiary education is striving to attain and maintain cultures of excellence, similar to traditional universities in the past ( 13 ). However, there has been a shift to turn modern day campuses into high stakes competitive testing environments with well-intended emphasis on preparing students to become part of the global economy. This change has influenced the context in which modern universities function. There are a set of challenges that contemporary universities face that extend beyond the earlier tertiary educational institutions and there is an assumption that students are coming to college “overwhelmed and more damaged than those of previous years” ( 14 ).

Although good citizenship has always been an important foundation of all educational institutions, with the dynamic social landscape that the universities are set within, there seems to be a tendency to lead students to fixate on extrinsic factors such as: results and Grade Point Averages, over intrinsic interest such as innovative learning, and expansion of lateral thinking ( 13 ). When the priority is grades, it manifests itself in excessive hours of focused studying, and in negative coping behaviors, such as: inadequate sleep and addictive behaviors, which could potentially affect the well-being of the student. Often, in this pursuit of academic excellence, there is the danger of ignoring the social, emotional, and psychological problems that modern students are now increasingly facing.

There is enough research that indicates that students are experiencing more mental health disorders in contemporary times and are less resilient than students in the past ( 8 ), with lower levels of frustration tolerance ( 15 ). Anxiety and depression are most prevalent among tertiary students ( 16 ). There is a rise in the number of college students with a diagnosable psychological disorder ( 17 ) with some students at greater risk than others of experiencing stress and mental health problems ( 18 ). There has been also a shift in the severity of the problems by students seeking counseling services over the past decade. It is no longer just presenting challenges of adjustment and individuation ( 19 ), or benign hormonal developmental problems associated with the age that prompts students to seek counseling. Students are presenting with severe psychological problems ( 20 ) with a sizeable number of them on psychiatric medication to help them function better on campus ( 15 ).

A common narrative through an exhaustive body of literature highlights the barriers to seeking help for mental health problems by students on campus due to stigma ( 21 ), scepticism about treatment efficacy ( 22 ), and a belief that their emotional problems will not be completely understood. This leads to a sense of social isolation as the students restrain from reaching out for help ( 21 ). Two contributing factors to inadequate help-seeking are the stigma of having a mental health problem and the personal characteristics of the individual student ( 20 ). A fear of negative consequences on academic records ( 23 ) is another common barrier among university students. Interestingly, students resist seeking help because they do not perceive their condition to require intervention or do not perceive it as a priority among their other commitments. They also have the tendency to normalize stress as part of university life, expecting it “will go away with time,” and prefer to handle their problems on their own ( 24 ).

More recent research indicates that students also rely on informal sources of help-seeking from non-professionals, particularly peer groups ( 25 ). Students report having no inhibitions about having open discussions about their mental health problems via social-networking websites ( 26 ). This resonates with the network episode model of help-seeking that emphasizes the social network as an integral, contemporary support in enhancing knowledge and attitudes toward seeking help ( 27 ). However, there is also a significant increase in the number of students with major psychological problems seeking counseling services on campus ( 3 ) challenging the stigma connected with help-seeking. The newer generation's familiarity with psychosocial support services and openness toward seeking them are putting mental health at the core of self-care, much like diet and exercise ( 26 ).

Along with rapid social changes and expectations, the dilution of traditional family anchors (that is the changes to family systems which include busy yet isolated lifestyles, social media pressures, a living free from parental influence which is very common to this age group, and forced separation from families in the pursuit of dream destinations for education) all compounding to the considerable degree of stress that students report upon ( 18 ). Considering all these transitions, focusing on the support that is available to young people on campus is increasingly becoming a necessity. This is not only a personal benefit for students but a national and international investment that could also result in considerable economic benefit ( 28 ) as these students stand to become contributors to the global economy.

A wealth of research exists which highlights the effectiveness of changing organizational factors that influence mental health ( 29 , 30 ). However, there is limited research on person-centric mental health strategies used in university settings ( 31 ). A Systematic Literature Review that was conducted by Fernandez et al. focused on evaluating the effect of setting-based interventions that stimulated and improved the mental health and well-being of university students and employees ( 32 ). That review constitutes an asset for universities seeking to adopt setting-based strategies that were proven efficacious. Yet, given the highspeed in which the higher education ecosystem has been evolving, there is an evident need for a more up-to-date review. Also, despite the importance of modifying the environment for it to become more nurturing for university students' mental health, this needs to be in conjunction with embracing the individuality of each student. Accordingly, the purpose of this study is to bridge this gap through providing a review of the literature on recently published individual student-level interventions that aim to alleviate the burden of mental health challenges faced by the students and/or help them with coping mechanisms that will foster their resilience.

We conducted a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines ( 33 ). The protocol of the systematic review was published in PROSPERO, a database of prospectively registered systematic reviews in health and social care (CRD42021227862).

Search Strategy

To complement the work of Fernandez et al., focusing on the recent literature, the search period was confined to July 2016 through December 2020 ( 32 ). PubMed database was used. The search strategy used, with its key words and Boolean logic, is available as an online resource. It was structured as follows:

• Subjects: student or resident.

• Location: higher education, university, college, or tertiary education.

• State-of-being : mental health.

• Challenges faced by subjects : psychosocial, anxiety, depression, burnout, stress, peer-pressure, social media pressure, bullying, eating disorder, perfectionism, or learning difficulties.

• Intervention to address the challenges : psychotherapy, mindfulness, Counseling, support group, yoga, breathing, art therapy, awareness, resilience, gratitude, affirmations, or peer-Counseling.

Pure qualitative studies were excluded. We included all quantitative studies, so long as they contained information on the impact of the intervention. These included those using experimental (i.e., randomized controlled trials) or observational (i.e., controlled trials without randomization, and pre-post and time series) approaches. Duplicated papers were excluded. Studies were screened for inclusion in three phases:

1. BN and FO went over all the abstracts, together, to remove the articles that certainly did not meet the inclusion criteria.

2. The full text of all the remaining abstracts were reviewed independently by BN and FO. The results were discussed. Any discrepancies were investigated and reflected upon until reaching consensus.

3. Finally, all remaining articles were thoroughly reviewed for summarizing purposes based on a preset template: research study objective, context, design, method, sample, intervention, and main conclusion.

Articles were included if:

a) Empirical/applied (i.e., theoretical studies or systematic reviews, and studies using secondary data were excluded),

b) Conducted in one or more university,

c) Aimed at evaluating, the immediate or long-term effect of an intervention on the mental health status of students,

d) Included global measures of mental health and well-being,

e) Had the university counselor involved in the intervention,

f) Involved full-time students, and

g) Was written in English.

Quality Assessment

The quality of each of the included articles was evaluated considering the internal and external validity. For the internal validity (risk of bias), each study's methodological quality was assessed using the criteria introduced by Jadad et al. ( 34 ). As for the external/ ecological validity of the included studies, it was assessed using the criteria developed by Green and Glasgow ( 35 ). This quality assessment was not used to exclude articles. Yet, the results of the assessment were thoroughly reflected upon as an evaluative measure of the review output.

Data Analysis

The interventions referred to in the included studies were analyzed by the researchers using the framework of Braun and Clarke ( 36 ). The intention was to inductively build a general interpretation of all included studies, in alignment with the paradigm of constructivism ( 37 , 38 ). The assumption was that reality is socially-constructed. This required thoroughly reflecting upon the interventions investigated in the included studies. The process of exploratory reflection adapted was spiral, where the researchers' observations kept getting revisited which culminated into the development of an evidence-driven model. Since the constructivism paradigm gives precedence to thoroughness and insightfulness over extensiveness and generalizability ( 39 ), the decision was made upfront, as abovementioned, for this search to be limited to a single database ( 40 ). As for the purpose of the qualitative meta-synthesis, it was to create a dynamic individual-level intervention framework that is holistic and context-specific ( 41 ). All articles were categorized based on the nature of the intervention(s) under investigation. It is all narratively presented in the results section.

A total of 1,399 records were identified by the electronic search. Two researchers (BN and FO) reviewed all the abstracts of the resulting papers to identify ones that fitted the inclusion criteria. Based on that, a total of 1,178 articles were excluded. The full text of all remaining 220 articles were extracted and thoroughly reviewed by the two researchers (110 by each). Accordingly, 133 articles were excluded. The remaining 87 articles underwent another round of assessment by both researchers together. Out of these 87 articles, 47 papers were excluded: four studies did not meet the eligibility criteria of having an intervention in them, 31 studies did not include assessing the effectiveness of an intervention,10 studies were not exclusively on university students, and 1 was not on full-time students. Also, one study was excluded because it was not counselor-led but outsourced. Out of the initially identified 1399 articles, 40 articles were finally included in the study ( Figure 1 ).

Figure 1 . PRISMA flow-diagram. Promoting university students' mental health: a systematic literature review introducing the 4M-Model of individual-level interventions, Dubai, United Arab Emirates, 2020.

Of the 40 studies, nine studies were conducted in USA, eight in United Kingdom, four in Canada, three in Australia, five in Germany, four in China, and one in each of Turkey, Hungary, Israel, Ireland, Japan, South Korea and Netherlands. The quality of evidence is very high in terms of internal validity because most of the studies ( 25 ) employed RCT, five studies used a quasi-experimental method, two had a cross sectional design, and eight studies utilized a pre-post design without a control group.

The external validity of the papers could be considered low/ moderate. Since most of the studies indicated the experience of only one institution; generalization of the findings is limited. The only exceptions were one study that was conducted in Israel which included three institutions and one conducted in UK which included eight universities. After thoroughly reflecting upon the interventions under investigation across all 40 resulting studies, the authors qualitatively synthesized a holistic framework. This involved inductively identifying four overlapping categories of interventions. Each category was in turn coded with a label that appeared to be most fit to the encapsulated interventions and that is in harmony with the codes of the rest of the categories (i.e., alliteration).

Accordingly, each study was linked to at least one of four overlapping categories based on the nature of the intervention(s) under investigation ( Table 1 ). The first category, coded as Mindfulness, included individual-level interventions that used mindfulness as a strategy to promote mental health. Mindfulness, in this context, refers to any intervention that aims to promote living in the moment or “now” and adopting acceptance and a non-judgmental attitude to guide action. The popular Mindfulness Based Stress Reduction (MBSR) curriculum was used in four studies ( 8 , 42 – 45 ). Mindfulness Based Cognitive Therapy (MBCT) which focuses on reframing thoughts along with becoming aware of the nature and quality of them was found to also be effective in two studies ( 46 , 47 ). In three studies, the intervention(s) made use of imagery and self-guidance ( 48 – 51 ), whereas two other studies explored the effectiveness of Acceptance and Commitment Therapy (ACT) ( 6 ) to improve the psychological flexibility, school engagement, and mental health among University students.

Table 1 . Distribution of the output of the systematic literature review depending on the nature of the intervention(s) under investigation.

The second category of studies was coded as Movement and included individual-level interventions which have a predominant physical element and solicit change in bodily sensations including but not limited to yoga, fitness, dance, kickboxing, and aerobics and breathing exercises. While Tong et al. ( 52 ) exclusively looked at the effect of Yoga and Fitness on mental health, five sets of researchers ( 8 , 42 , 43 , 45 , 46 ) looked at breathing and simple yoga as part of their mindfulness course. Sleep was studied in connection to mental health in two studies ( 53 , 54 ) as it has been found to be a precursor to many mental health problems with insomnia and the quality of sleep put on top of the list affecting sleep hygiene. Behavioral activation, a personalized therapeutic tool mainly used in the treatment of depression targeting behaviors that feed into the condition, was found to be effective in three studies that were reviewed ( 55 – 57 ) involving students with mild depression. The goal of Behavioral Activation is engaging in enjoyable activities with a part of the process focusing on getting past obstacles that may impede that enjoyment. One study included peer-led support ( 56 ) and online delivery of the course ( 57 ), where both appeared to be efficacious. Only one study by Chalo et al. ( 58 ) used Biofeedback intervention, that involved measuring students' quantifiable bodily functions to convey information to them in real-time as a solution to help students manage their physiological response to anxiety and stress.

The third category was coded as Meaning and included studies that investigate individual-level interventions that focus on the counselor addressing connections and associations between variables and enabling the student to reframe cognitions. Psychoeducation was widely utilized with cognitive training as the most common ( 54 , 59 – 63 ). Eustis et al. ( 49 ) focused their study on the student's self-awareness, while Demir and Ercan ( 64 ) explored communication techniques among students. In addition, three studies explored the feasibility of having courses embedded within the curriculum ( 38 , 48 , 50 ) to improve the mental health of students, while nine studies explored the effect of elective courses that aimed at stress reduction ( 18 , 43 , 50 , 56 , 58 , 65 – 69 ).

The last category of studies was coded as Moderator which referred to any element of support that was deployed in conjunction with the counselor, in an individual-level intervention, that acts as a moderator between the student and the counselor. Pet therapy was explored in three studies ( 70 – 72 ) to assess well-being, and an extensive use of the computer to deliver courses such as ACT, Psychoeducation, and Cognitive Behavior Therapy (CBT) which are all traditionally effective in psychotherapy, were found to be efficacious online in 10 studies ( 44 , 50 , 57 , 61 , 73 – 78 ) highlighting the significance of the potential of web-based interventions to impart psychotherapy to a wider audience.

This literature review showed that elements of Mindfulness were a major part of the 23 studies, Meaning was predominant in 24 studies, while Movement was an important feature in 17 studies. An element of support complementary to the therapist, either in the form of a pet (canine) or a web/phone application (i.e., Moderator), was part of 16 interventions. Commonly used approaches were Mindfulness based therapies, ACT, Cognitive Behavior Therapy, and Psychoeducation. The duration of the interventions investigated in the included studies ranged between 1 and 12 weeks, with most of the studies spanning between 6 and 8 weeks. Nine studies had just one element, and only one study ( 49 ) had all the four elements included ( Figure 2 ), which the authors perceived as a “lucky find.”

Figure 2 . The 4M-Model generated from this study's qualitative synthesis, visually illustrated as a four-leaf clover which is a symbol of luck. Promoting university students' mental health: a systematic literature review introducing the 4M-Model of individual-level interventions, Dubai, United Arab Emirates, 2020.

Thirty-one studies had overlapping elements indicating that these elements are not mutually exclusive and rather interlinked and are blended with the intention of enhancing the effectiveness of a program.

The output of this Systematic Literature Review revealed diverse interventions. Most of these interventions were hybrid versions of existing evidence-based interventions. A few of the identified articles reflected upon contextualized home-grown interventions. There appeared to be a lack of consensus on a common model/ approach to effectively improve the mental health and wellness of university students ( 61 ) who are known to have their own set of challenges. Hence, this paper provides an outline of practices that have been deployed in this direction, illustrating them from a holistic perspective. Elements of mindfulness, meaning, movement, and use of a moderator were seen to overlap in the studies. The blending of these elements was proven to be effective in improving metacognitive awareness, emotional regulation ( 79 ), concentration, and mental clarity ( 80 ), and decreasing emotional reactivity ( 81 ) and rumination (through disengagement with persistent negative thoughts) ( 82 ) and in turn reducing depression, stress, and anxiety ( 83 ). It has also shown to foster social connectedness and the ability to express oneself in various social situations ( 84 ) thereby reducing stress and anxiety and increasing patience, gratitude, and body awareness ( 85 ). With so many elements that need to be taken into consideration, the researchers have attempted to comprehend the output of this review from the field theory point-of-view where the “organism and environment are perceived as part of an interacting field” ( 86 ).

Moreover, Counseling strategies and interventions are meant to emphasize on the growth of an individual. The human potential for self-actualization, a concept understood by Abraham Maslow as a change process that aims at making a person “aware of what is going on inside himself” [Maslow, as cited in Seaman ( 87 ), p. 3] is core to Counseling interventions, which is where the four elements blend to become crucial to the process of self-awareness and eventually self-growth.

The results of the study indicate that self-awareness through mindfulness is an important foundation upon which all other elements build up to improve mental health of students. This was not a surprising find because this is in alignment with the results of many previously conducted studies ( 88 , 89 ). Mindfulness seems to be the new mantra and has been intensively researched ( 90 ). However, despite a substantial amount of theoretical work conducted to merge Buddhist and Western conceptual viewpoints to psychotherapy ( 91 ), there is minimal literature on how it can translate to practice making this review an important addition to the limited knowledge around the topic of psychological interventions that have been found to be effective among university students. MBSR has proven to reduce stress and anxiety among university students by fostering insight and concentration along with physiologic relaxation ( 92 ). Teaching students to live in the present moment by reframing thoughts (i.e., MBCT) has been found to be effective in reducing depression ( 93 ). It also lessens the risk of relapse with comparable efficacy to antidepressant medication ( 94 ) which, in itself, is a breakthrough for psychotherapy. ACT which focuses on acceptance has been found to improve coping, self-regulation, psychological flexibility, and school engagement ( 6 ). Counseling young adults, in particular students at the university level, would benefit by basing it on Engel's biopsychosocial viewpoint which includes taking into consideration the hormonal changes (biological), identity crisis, and the challenges arising from intimacy and isolation (psychological) which have been hypothesized in Eric Erickson's psychosocial stages of development for this age group. The new age technological challenges of peer-pressure over social media sites and the demands of fitting in and changing family dynamics (sociological) also need to be taken into consideration when conceptualizing a Counseling program for this target group.

Moreover, this transition stage between adolescence and adulthood, also referred to as “emerging adulthood” ( 95 ), is considered to be a period of accepting responsibility for one's actions and livelihood, developing belief systems and values independent of parental and external influences, and establishing relationships with parents on equal grounds. Young university students who are still financially dependent and living with parents during this period are arbitrarily considered to be adolescents if adult responsibilities are not yet accessed. These intangible markers gradually develop. The entailed process could last many years until the corresponding responsibilities are effectively adopted. As such, the range between adolescence and adulthood becomes wider than typically defined, stretching from the beginning of puberty to the early twenties ( 96 ).

Counseling has been traditionally associated as a profession that requires the physical presence of a minimum of two people in a professional relationship to talk through and process experiences to gain insight and understanding. However, in this review, it is evident that web-based interventions seem to produce an equally effective result ( 97 ) as observed in 16 studies of the literature review which could be utilized as a complementary medium widening the scope of practice of counselors and psychotherapists. This could also help in minimizing the stigma associated with getting undesirably labeled and help in reducing psychological self-restraint which has been termed as ‘online disinhibition effect' ( 98 ). Web-based mental health interventions also are becoming a preferred medium for students to gain services and information ( 99 ) as they accommodate their busy schedules ( 100 ).

Another observation was that even though most of the interventions were conducted only for a short period of time, the effectiveness of the interventions was established. Embedding interventions within the curriculum has been suggested ( 101 ) which makes this review even more pertinent for innovations in curriculum planning. This may also help in alleviating the stigma that is attached to Counseling services which is often a barrier that prevents students from reaching out for help ( 102 ). This aligns with Vygotsky's notion of Zone of Proximal Development ( 103 ) which refers to pedagogical support being beneficial for activities, in this context, psychoeducation of positive behaviors that facilitate help seeking behaviors before they can start using them independently.

The above observations prompted the researchers to recognize that the four identified elements when combined would result in a holistic approach of addressing the individual from a biopsychosocial point-of-view. This was depicted in the form of the 4M-Model to guide counselors to develop and implement university-level interventions that could help to reduce stress, anxiety, and depression as well as improve emotion regulation and self-awareness to address the mental health needs of young adults. It would be worthwhile for future research studies to validate the suggested 4M-Model through a similar systematic review of the literature relying on a combination of databases ( 104 ). The analysis in this case would be deductive where the model conceived from this study can be used as a preset template. Also, for validation purposes, it is recommended to conduct follow-up studies aimed at evaluating the efficaciousness of a tailor-made assortment of interventions that can be linked to all elements of the 4M-Model. For that purpose, it would be useful to adapt a mixed methods approach to research, where quantitative and qualitative findings will be integrated to obtain a holistic perspective of the output, outcome, and impact of such university-based, individual-student level mental health initiatives.

Findings of this review reveal the 4M-Model that happen to address all aspects of holistic well-being: physical, psychological, emotional, and social. Effectiveness of the varied interventions that have been reviewed in this study indicate that if a comprehensive approach toward intervention including mindfulness, movement, moderator, and meaning is adapted, then it would not only help students to be supported in a holistic manner but would help counselors plan and execute their programs in a focused approach to address the needs of any university student population who are increasingly overwhelmed and burned out with the stressors from their outside worlds as well as from within. The findings from the review add to the growing evidence for the urgent need of an intervention model that can serve as a directive for counselors and students.

Data Availability Statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.

Author Contributions

BN and FO conceptualized the study, conducted the review, performed the qualitative meta-synthesis, and prepared and approved the manuscript. Both authors contributed to the article and approved the submitted version.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

The authors would like to extensd their gratitude to three of their colleagues: Dr. Lisa Jackson, Dr. Leigh Powell, and Ms. Mersiha Kovacevic, for their active role, and valuable reflections and feedback in reviewing the complete manuscript.

1. WHO. Gender and Women's Health . World Health Organization (2009). Available from: http://www.who.int/mental_health/prevention/genderwomen/en/ (accessed May 11, 2021).

2. Breslau J, Lane M, Sampson N, Kessler RC. Mental disorders and subsequent educational attainment in a US national sample. J Psychiatr Res. (2008) 42:708–16. doi: 10.1016/j.jpsychires.2008.01.016

PubMed Abstract | CrossRef Full Text | Google Scholar

3. Kitzrow MA. The mental health needs of today's college students: challenges and recommendations. NASPA J . (2003) 41:167–81. doi: 10.2202/1949-6605.1310

CrossRef Full Text | Google Scholar

4. Thomas MR, Dyrbye LN, Huntington JL, Lawson KL, Novotny PJ, Sloan JA, et al. How do distress and well-being relate to medical student empathy? A multicenter study. J Gen Intern Med. (2007) 22:177–83. doi: 10.1007/s11606-006-0039-6

5. Byrd DR, McKinney KJ. Individual, interpersonal, and institutional level factors associated with the mental health of college students. J Am Coll Health. (2012) 60:185–93. doi: 10.1080/07448481.2011.584334

6. Gregoire S, Lachance L, Bouffard T, Dionne F. The use of acceptance and commitment therapy to promote mental health and school engagement in university students: a multisite randomized controlled trial. Behav Ther. (2018) 49:360–72. doi: 10.1016/j.beth.2017.10.003

7. Seligman MEP, Ernst RM, Gillham J, Reivich K, Linkins M. Positive education: positive psychology and classroom interventions. Oxford Rev Educ. (2009) 35:293–311. doi: 10.1080/03054980902934563

8. Galante J, Dufour G, Vainre M, Wagner AP, Stochl J, Benton A, et al. A mindfulness-based intervention to increase resilience to stress in university students (the Mindful Student Study): a pragmatic randomised controlled trial. Lancet Public Health. (2018) 3:e72–81. doi: 10.1016/S2468-2667(17)30231-1

9. Reavley NJ, Jorm AF. Recognition of mental disorders and beliefs about treatment and outcome: findings from an Australian national survey of mental health literacy and stigma. Aust N Z J Psychiatry. (2011) 45:947–56. doi: 10.3109/00048674.2011.621060

10. Cvetkovski S, Reavley NJ, Jorm AF. The prevalence and correlates of psychological distress in Australian tertiary students compared to their community peers. Aust N Z J Psychiatry. (2012) 46:457–67. doi: 10.1177/0004867411435290

11. Hamaideh SH. Stressors and reactions to stressors among university students. Int J Soc Psychiatry. (2011) 57:69–80. doi: 10.1177/0020764009348442

12. Tupler LA, Hong JY, Gibori R, Blitchington TF, Krishnan KR. Suicidal ideation and sex differences in relation to 18 major psychiatric disorders in college and university students: anonymous web-based assessment. J Nerv Ment Dis. (2015) 203:269–78. doi: 10.1097/NMD.0000000000000277

13. Oades LG, Robinson P, Green S, Spence GB. Towards a positive university. In: Positive Psychology in Higher Education . Routledge (2014). p. 15–22. doi: 10.4324/9781315829692-7

14. Levine A, Cureton JS. What we know: about today's college students. Education. (1998) 3:4–9. doi: 10.1177/108648229800300103

15. Gallagher RP, Gill A, Sysko H. National Survey of Counseling Centre Directors (2000).

Google Scholar

16. Center for Collegiate Mental Health. 2017 Annual Report . (Publication No. STA 18-166) (2018).

17. Blanco C, Okuda M, Wright C, Hasin DS, Grant BF, Liu SM, et al. Mental health of college students and their non-college-attending peers: results from the National Epidemiologic Study on Alcohol and Related Conditions. Arch Gen Psychiatry. (2008) 65:1429–37. doi: 10.1001/archpsyc.65.12.1429

18. Nguyen-Feng VN, Greer CS, Frazier P. Using online interventions to deliver college student mental health resources: evidence from randomized clinical trials. Psychol Serv. (2017) 14:481–9. doi: 10.1037/ser0000154

19. Pledge DS, Lapan RT, Heppner PP, Kivlighan D, Roehlke HJ. Stability and severity of presenting problems at a university counseling center: a 6-year analysis. Prof Psychol Res Pract. (1998) 29:386–9. doi: 10.1037/0735-7028.29.4.386

20. Storrie K, Ahern K, Tuckett A. A systematic review: students with mental health problems–a growing problem. Int J Nurs Pract. (2010) 16:1–6. doi: 10.1111/j.1440-172X.2009.01813.x

21. Megivern D, Pellerito S, Mowbray C. Barriers to higher education for individuals with psychiatric disabilities. Psychiatr Rehabil J. (2003) 26:217–31. doi: 10.2975/26.2003.217.231

22. Eisenberg D, Downs MF, Golberstein E, Zivin K. Stigma and help seeking for mental health among college students. Med Care Res Rev. (2009) 66:522–41. doi: 10.1177/1077558709335173

23. Tjia J, Givens JL, Shea JA. Factors associated with undertreatment of medical student depression. J Am Coll Health. (2005) 53:219–24. doi: 10.3200/JACH.53.5.219-224

24. Eisenberg D, Hunt J, Speer N, Zivin K. Mental health service utilization among college students in the United States. J Nerv Ment Dis. (2011) 199:301–8. doi: 10.1097/NMD.0b013e3182175123

25. Repper J, Carter T. A review of the literature on peer support in mental health services. J Mental Health. (2011) 20:392–411. doi: 10.3109/09638237.2011.583947

26. Eisenberg D, Speer N, Hunt JB. Attitudes and beliefs about treatment among college students with untreated mental health problems. Psychiatr Serv. (2012) 63:711–3. doi: 10.1176/appi.ps.201100250

27. Pescosolido BA, Boyer CA. Understanding the context and dynamic social processes of mental health treatment. In: A Handbook for the Study of Mental Health: Social Contexts, Theories, and Systems , Vol. 2 (2010). p. 420–38. doi: 10.1017/CBO9780511984945.026

28. Patton GC, Sawyer SM, Santelli JS, Ross DA, Afifi R, Allen NB, et al. Our future: a lancet commission on adolescent health and wellbeing. Lancet. (2016) 387:2423–78. doi: 10.1016/S0140-6736(16)00579-1

29. Conley CS, Durlak JA, Kirsch AC. A meta-analysis of universal mental health prevention programs for higher education students. Prev Sci. (2015) 16:487–507. doi: 10.1007/s11121-015-0543-1

30. Davies EB, Morriss R, Glazebrook C. Computer-delivered and web-based interventions to improve depression, anxiety, and psychological well-being of university students: a systematic review and meta-analysis. J Med Internet Res. (2014) 16:e130. doi: 10.2196/jmir.3142

31. Dooris M. Holistic and sustainable health improvement: the contribution of the settings-based approach to health promotion. Perspect Public Health. (2009) 129:29–36. doi: 10.1177/1757913908098881

32. Fernandez A, Howse E, Rubio-Valera M, Thorncraft K, Noone J, Luu X, et al. Setting-based interventions to promote mental health at the university: a systematic review. Int J Public Health. (2016) 61:797–807. doi: 10.1007/s00038-016-0846-4

33. Moher D, Liberati A, Tetzlaff J, Altman DG, Group P. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. (2009) 6:e1000097. doi: 10.1371/journal.pmed.1000097

34. Jadad AR, Moore RA, Carroll D, Jenkinson C, Reynolds DJ, Gavaghan DJ, et al. Assessing the quality of reports of randomized clinical trials: is blinding necessary? Control Clin Trials. (1996) 17:1–12. doi: 10.1016/0197-2456(95)00134-4

35. Green LW, Glasgow RE. Evaluating the relevance, generalization, and applicability of research: issues in external validation and translation methodology. Eval Health Prof. (2006) 29:126–53. doi: 10.1177/0163278705284445

36. Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol. (2006) 3:2:77–101. doi: 10.1191/1478088706qp063oa

37. Theoret C, Ming X. Our education, our concerns: the impact on medical student education of COVID-19. Med Educ. (2020) 54:591–2. doi: 10.1111/medu.14181

38. Gordon M. Are we talking the same paradigm? Considering methodological choices in health education systematic review. Med Teach. (2016) 38:746–50. doi: 10.3109/0142159X.2016.1147536

39. Gheondea-Eladi A. Is qualitative research generalizable? Jurnalul Practicilor Comunitare Pozitive. (2014) 14:114–24.

40. Seaman J, Dettweiler U, Humberstone B, Martin B, Prince H, Quay J. Joint recommendations on reporting empirical research in outdoor, experiential, environmental, and adventure education journals. J Exp Educ. (2020) 43:348–64. doi: 10.1177/1053825920969443

41. Yilmaz K. Comparison of Quantitative and Qualitative Research Traditions: epistemological, theoretical, and methodological differences. Eur J Educ. (2013) 48:311–25.

42. Falsafi N. A randomized controlled trial of mindfulness versus yoga: effects on depression and/or anxiety in college students. J Am Psychiatr Nurses Assoc. (2016) 22:483–97. doi: 10.1177/1078390316663307

43. van Dijk I, Lucassen PL, Akkermans RP, van Engelen BG, van Weel C, Speckens AE. Effects of mindfulness-based stress reduction on the mental health of clinical clerkship students: a cluster-randomized controlled trial. AEMAcad Med. (2017) 92:1012–21. doi: 10.1097/ACM.0000000000001546

44. Malpass A, Binnie K, Robson L. Medical students' experience of mindfulness training in the UK: well-being, coping reserve, and professional development. Educ Res Int. (2019) 2019. doi: 10.1155/2019/4021729

45. Alkoby A, Pliskin R, Halperin E, Levit-Binnun N. An eight-week mindfulness-based stress reduction (MBSR) workshop increases regulatory choice flexibility. Emotion. (2019) 19:593–604. doi: 10.1037/emo0000461

46. O'Driscoll M, Sahm LJ, Byrne H, Lambert S, Byrne S. Impact of a mindfulness-based intervention on undergraduate pharmacy students' stress and distress: quantitative results of a mixed-methods study. Curr Pharm Teach Learn. (2019) 11:876–87. doi: 10.1016/j.cptl.2019.05.014

47. Gu Y, Xu G, Zhu Y. A randomized controlled trial of mindfulness-based cognitive therapy for college students with ADHD. J Atten Disord. (2018) 22:388–99. doi: 10.1177/1087054716686183

48. Hall BJ, Xiong P, Guo X, Sou EKL, Chou UI, Shen Z. An evaluation of a low intensity mHealth enhanced mindfulness intervention for Chinese university students: a randomized controlled trial. Psychiatry Res. (2018) 270:394–403. doi: 10.1016/j.psychres.2018.09.060

49. Eustis EH, Hayes-Skelton SA, Orsillo SM, Roemer L. Surviving and thriving during stress: a randomized clinical trial comparing a brief web-based therapist-assisted acceptance-based behavioral intervention versus waitlist control for college students. Behav Ther. (2018) 49:889–903. doi: 10.1016/j.beth.2018.05.009

50. Cavanagh K, Churchard A, O'Hanlon P, Mundy T, Votolato P, Jones F, et al. A randomised controlled trial of a brief online mindfulness-based intervention in a non-clinical population: replication and extension. Mindfulness. (2018) 9:1191–205. doi: 10.1007/s12671-017-0856-1

51. Dvorakova K, Kishida M, Li J, Elavsky S, Broderick PC, Agrusti MR, et al. Promoting healthy transition to college through mindfulness training with first-year college students: pilot randomized controlled trial. J Am Coll Health. (2017) 65:259–67. doi: 10.1080/07448481.2017.1278605

52. Tong J, Qi X, He Z, Chen S, Pedersen SJ, Cooley PD, et al. The immediate and durable effects of yoga and physical fitness exercises on stress. J Am Coll Health. (2020) 11:1–9. doi: 10.1080/07448481.2019.1705840

53. Friedrich A, Classen M, Schlarb AA. Sleep better, feel better? Effects of a CBT-I and HT-I sleep training on mental health, quality of life and stress coping in university students: a randomized pilot controlled trial. BMC Psychiatry. (2018) 18:268. doi: 10.1186/s12888-018-1860-2

54. Morris J, Firkins A, Millings A, Mohr C, Redford P, Rowe A. Internet-delivered cognitive behavior therapy for anxiety and insomnia in a higher education context. Anxiety Stress Coping. (2016) 29:415–31. doi: 10.1080/10615806.2015.1058924

55. Takagaki K, Okamoto Y, Jinnin R, Mori A, Nishiyama Y, Yamamura T, et al. Behavioral activation for late adolescents with subthreshold depression: a randomized controlled trial. Eur Child Adolesc Psychiatry. (2016) 25:1171–82. doi: 10.1007/s00787-016-0842-5

56. Byrom N. An evaluation of a peer support intervention for student mental health. J Ment Health. (2018) 27:240–6. doi: 10.1080/09638237.2018.1437605

57. Puspitasari AJ, Kanter JW, Busch AM, Leonard R, Dunsiger S, Cahill S, et al. A randomized controlled trial of an online, modular, active learning training program for behavioral activation for depression. J Consult Clin Psychol. (2017) 85:814–25. doi: 10.1037/ccp0000223

58. Chalo P, Pereira A, Batista P, Sancho L. Brief biofeedback intervention on anxious freshman university students. Appl Psychophysiol Biofeedback. (2017) 42:163–8. doi: 10.1007/s10484-017-9361-5

59. Bettis AH, Coiro MJ, England J, Murphy LK, Zelkowitz RL, Dejardins L, et al. Comparison of two approaches to prevention of mental health problems in college students: enhancing coping and executive function skills. J Am Coll Health. (2017) 65:313–22. doi: 10.1080/07448481.2017.1312411

60. Biro E, Veres-Balajti I, Adany R, Kosa K. Social cognitive intervention reduces stress in Hungarian university students. Health Promot Int. (2017) 32:73–8. doi: 10.1093/heapro/dau006

61. Farrer LM, Gulliver A, Katruss N, Fassnacht DB, Kyrios M, Batterham PJ. A novel multi-component online intervention to improve the mental health of university students: randomised controlled trial of the Uni Virtual Clinic. Internet Interv. (2019) 18:100276. doi: 10.1016/j.invent.2019.100276

62. Kotter T, Niebuhr F. Resource-oriented coaching for reduction of examination-related stress in medical students: an exploratory randomized controlled trial. Adv Med Educ Pract. (2016) 7:497–504. doi: 10.2147/AMEP.S110424

63. Kuhlmann SM, Huss M, Burger A, Hammerle F. Coping with stress in medical students: results of a randomized controlled trial using a mindfulness-based stress prevention training (MediMind) in Germany. BMC Med Educ. (2016) 16:316. doi: 10.1186/s12909-016-0833-8

64. Demir S, Ercan F. The effect of a self-awareness and communication techniques course on the communication skills and ways of coping with stress of nursing students: An interventional study in Ankara, Turkey. J Pak Med Assoc. (2019) 69:659–65.

PubMed Abstract

65. Shannon S, Hanna D, Haughey T, Leavey G, McGeown C, Breslin G. Effects of a mental health intervention in athletes: applying self-determination theory. Front Psychol. (2019) 10:1875. doi: 10.3389/fpsyg.2019.01875

66. Tolgou T, Rohrmann S, Stockhausen C, Krampen D, Warnecke I, Reiss N. Physiological and psychological effects of imagery techniques on health anxiety. Psychophysiology. (2018) 55:e12984. doi: 10.1111/psyp.12984

67. Scholz M, Neumann C, Wild K, Garreis F, Hammer CM, Ropohl A, et al. Teaching to relax: development of a program to potentiate stress-results of a feasibility study with medical undergraduate students. Appl Psychophysiol Biofeedback. (2016) 41:275–81. doi: 10.1007/s10484-015-9327-4

68. Guo YF, Zhang X, Plummer V, Lam L, Cross W, Zhang JP. Positive psychotherapy for depression and self-efficacy in undergraduate nursing students: a randomized, controlled trial. Int J Ment Health Nurs. (2017) 26:375–83. doi: 10.1111/inm.12255

69. Kim S KH, Lee H, Lee H, Noh D. Effectiveness of a brief stress management intervention in male college students. Perspect Psychiatr Care. (2018) 54:88–94. doi: 10.1111/ppc.12212

70. Grajfoner D, Harte E, Potter LM, McGuigan N. The effect of dog-assisted intervention on student well-being, mood, and anxiety. Int J Environ Res Public Health. (2017) 14:483. doi: 10.3390/ijerph14050483

71. Wood E, Ohlsen S, Thompson J, Hulin J, Knowles L. The feasibility of brief dog-assisted therapy on university students stress levels: the PAwS study. J Ment Health. (2018) 27:263–8. doi: 10.1080/09638237.2017.1385737

72. Binfet JT PH, Cebry A, Struik K, McKay C. Reducing university students' stress through a drop-in canine-therapy program. J Mental Health. (2018) 27:197–204. doi: 10.1080/09638237.2017.1417551

73. Viskovich S, Pakenham KI. Pilot evaluation of a web-based acceptance and commitment therapy program to promote mental health skills in university students. J Clin Psychol. (2018) 74:2047–69. doi: 10.1002/jclp.22656

74. McCall HC, Richardson CG, Helgadottir FD, Chen FS. Evaluating a web-based social anxiety intervention among university students: randomized controlled trial. J Med Int Res. (2018) 20:e91. doi: 10.2196/jmir.8630

75. Lee RA, Jung ME. Evaluation of an mHealth App (DeStressify) on university students' mental health: pilot trial. JMIR Ment Health. (2018) 5:e2. doi: 10.2196/mental.8324

76. Viskovich S, Pakenham KI. Randomized controlled trial of a web-based Acceptance and Commitment Therapy (ACT) program to promote mental health in university students. J Clin Psychol. (2020) 76:929–51. doi: 10.1002/jclp.22848

77. Fulmer R, Joerin A, Gentile B, Lakerink L, Rauws M. Using psychological artificial intelligence (tess) to relieve symptoms of depression and anxiety: randomized controlled trial. JMIR Ment Health. (2018) 5:e64. doi: 10.2196/mental.9782

78. Juncos DG, Heinrichs GA, Towle P, Duffy K, Grand SM, Morgan MC, et al. Acceptance and commitment therapy for the treatment of music performance anxiety: a pilot study with student vocalists. Front Psychol. (2017) 8:986. doi: 10.3389/fpsyg.2017.00986

79. Farb NA, Anderson AK, Mayberg H, Bean J, McKeon D, Segal ZV. Minding one's emotions: mindfulness training alters the neural expression of sadness. Emotion. (2010) 10:25–33. doi: 10.1037/a0017151

80. Moore A, Malinowski P. Meditation, mindfulness and cognitive flexibility. Conscious Cogn. (2009) 18:176–86. doi: 10.1016/j.concog.2008.12.008

81. Ortner CNM, Kilner SJ, Zelazo PD. Mindfulness meditation and reduced emotional interference on a cognitive task. Motiv Emot. (2007) 31:271–83. doi: 10.1007/s11031-007-9076-7

82. Chambers R, Lo BC, Allen NB. The impact of intensive mindfulness training on attentional control, cognitive style, and affect. Cogn Ther Res. (2008) 32:303–22. doi: 10.1007/s10608-007-9119-0

83. Cohen JS, Miller L. Interpersonal mindfulness training for well-being: a pilot study with psychology graduate students. Teach Coll Rec. (2009) 111:2760–74.

84. Dekeyser M, Raes F, Leijssen M, Leyson S, Dewulf D. Mindfulness skills and interpersonal behavior. Person Ind Differ. (2008) 44:1235–45. doi: 10.1016/j.paid.2007.11.018

85. Rothaupt JW, Morgan MM. Counselors' and counselor educators' practice of mindfulness: a qualitative inquiry. Counsel Values. (2007) 52:40–54. doi: 10.1002/j.2161-007X.2007.tb00086.x

86. Bourgault du Coudray C. Theory and praxis in experiential education: some insights from gestalt therapy. J Exp Educ. (2020) 43:156–70. doi: 10.1177/1053825920904387

87. Seaman J. Is group therapy democratic? enduring consequences of outward Bound's alignment with the human potential movement. A response to “how to be nice and get what you want: structural referents of ‘Self' and ‘Other' in experiential education as (Un) democratic practice. Democr. Educ. (2016) 24:13. Available online at: https://democracyeducationjournal.org/home/vol24/iss2/13

88. Groves P. Mindfulness in psychiatry - where are we now? BJPsych Bull. (2016) 40:289–92. doi: 10.1192/pb.bp.115.052993

89. Walsh R, Shapiro SL. The meeting of meditative disciplines and Western psychology: a mutually enriching dialogue. Am Psychol. (2006) 61:227–39. doi: 10.1037/0003-066X.61.3.227

90. Powell A. When Science Meets Mindfulness: The Harvard Gazette (2018. Available from: https://news.harvard.edu/gazette/story/2018/04/harvard-researchers-study-how-mindfulness-may-change-the-brain-in-depressed-patients/ (accessed May 11, 2021).

91. Epstein M. Psychotherapy Without the Self: A Buddhist Perspective . Yale University Press (2008).

92. Rosenzweig S, Reibel DK, Greeson JM, Brainard GC, Hojat M. Mindfulness-based stress reduction lowers psychological distress in medical students. Teach Learn Med. (2003) 15:88–92. doi: 10.1207/S15328015TLM1502_03

93. Tickell A, Ball S, Bernard P, Kuyken W, Marx R, Pack S, et al. The effectiveness of mindfulness-based cognitive therapy (MBCT) in real-world healthcare services. Mindfulness. (2020) 11:279–90. doi: 10.1007/s12671-018-1087-9

94. Kuyken W, Warren FC, Taylor RS, Whalley B, Crane C, Bondolfi G, et al. Efficacy of mindfulness-based cognitive therapy in prevention of depressive relapse: an individual patient data meta-analysis from randomized trials. JAMA Psychiatry. (2016) 73:565–74. doi: 10.1001/jamapsychiatry.2016.0076

95. Arnett JJ. Are college students adults? Their conceptions of the transition to adulthood. J Adult Dev. (1994) 1:213–24. doi: 10.1007/BF02277582

96. Arnett J. Reckless behavior in adolescence: a developmental perspective. Dev Rev. (1992) 12:339–73. doi: 10.1016/0273-2297(92)90013-R

97. Astramovich R, Jones W, Coker K. Technology-Enhanced Consultation in Counselling: A Comparative Study. Guid Counsel. (2004) 19:72–80.

98. Suler J. The online disinhibition effect. Cyberpsychol Behav. (2004) 7:321–6. doi: 10.1089/1094931041291295

99. Ryan ML, Shochet IM, Stallman HM. Universal online interventions might engage psychologically distresseduniversity students who are unlikely to seek formal help. Adv Mental Health. (2010) 9:73–83. doi: 10.5172/jamh.9.1.73

100. Amstadter AB, Broman-Fulks J, Zinzow H, Ruggiero KJ, Cercone J. Internet-based interventions for traumatic stress-related mental health problems: a review and suggestion for future research. Clin Psychol Rev. (2009) 29:410–20. doi: 10.1016/j.cpr.2009.04.001

101. Chodkiewicz ARB. Positive psychology school-based interventions: a reflection on current success and future directions. Rev Educ. (2017) 5:60–86. doi: 10.1002/rev3.3080

102. Sullivan C, Mancillas A. Stigma toward seeking mental health services among graduate counseling students. VISTAS. (2015) 83.

103. Vygotsky LS. Mind in Society: The Development of Higher Psychological Processes . Harvard University Press (1980). doi: 10.2307/j.ctvjf9vz4.5

104. Bramer WM, Rethlefsen ML, Kleijnen J, Franco OH. Optimal database combinations for literature searches in systematic reviews: a prospective exploratory study. Syst Rev. (2017) 6:245. doi: 10.1186/s13643-017-0644-y

PubMed Abstract | CrossRef Full Text

Keywords: mental health, health and well-being, holism, university students, mindfulness, higher education, student support

Citation: Nair B and Otaki F (2021) Promoting University Students' Mental Health: A Systematic Literature Review Introducing the 4M-Model of Individual-Level Interventions. Front. Public Health 9:699030. doi: 10.3389/fpubh.2021.699030

Received: 22 April 2021; Accepted: 31 May 2021; Published: 25 June 2021.

Reviewed by:

Copyright © 2021 Nair and Otaki. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Bhavana Nair, bhavana.nair@mbru.ac.ae

† ORCID: Bhavana Nair orcid.org/0000-0002-3381-8293 Farah Otaki orcid.org/0000-0002-8944-4948

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Mental Health Screening: Recommendations from an Integrated Literature Review

• Systematic Review
• Published: 02 May 2024

Cite this article

• Victor Villarreal   ORCID: orcid.org/0000-0002-5612-3849 1 &
• Lisa S. Peterson   ORCID: orcid.org/0000-0001-9172-7608 2  

50 Accesses

Explore all metrics

School-based, multiple gate mental health screening has been identified as a major component of social, emotional, and behavioral systems of support models, and a promising practice that can be used to address unmet mental health needs of children and adolescents. To better inform implementation of multiple gate screening programs, we completed an integrated literature review based on a review of 38 school-based screening studies identified through a systematic review process. The focus of the review was on effective and ineffective screening strategies – and general screening considerations – presented in the identified studies. Considerations and implementation strategies related to mental health screening in schools, including issues related to consent and student participation, screening measures, how to integrate screening into school programs, how to manage suicide risk screening, and how to support students after screening are discussed. Considerations for future research are also discussed. Multiple-stage school-based mental health screening can improve identification of mental health needs and access to mental health services. However, special consideration must be given to implementing screening, and additional areas of research are needed to further our knowledge and practice recommendations in this area.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

School-Based Screening for Mental Health in Early Childhood

Conducting universal complete mental health screening via student self-report.

Feasibility of School-Based Identification of Children and Adolescents Experiencing, or At-risk of Developing, Mental Health Difficulties: a Systematic Review

References marked with an asterisk indicated studies included in the integrated review.

Abrams, Z. (2023, January 1). Kids’ mental health is in crisis. Here’s what psychologists are doing to help. Monitor on Psychology, 54(1) . Retrieved from https://www.apa.org/monitor/2023/01/trends-improving-youth-mental-health .

Anderson, K. N., Swedo, E. A., Trinh, E., Ray, C. M., Krause, K. H., Verlenden, J. V., Clayton, H. B., Villaveces, A., Massetti, G. M., & Niolon, H., P (2022). Adverse childhood experiences during the COVID-19 pandemic and associations with poor mental health and suicidal behaviors among high school students - adolescent behaviors and experiences Survey, United States, January-June 2021. MMWR Morbidity and Mortality Weekly Report , 71 (41), 1301–1305. https://doi.org/10.15585/mmwr.mm7141a2 .

Article   PubMed   PubMed Central   Google Scholar  

*August, G. J., Ostrander, R., & Bloomquist, M. J. (1992). Attention deficit hyperactivity disorder: An epidemiological screening method. American Journal of Orthopsychiatry , 62 (3), 387–396. https://doi.org/10.1037/h0079354 .

Article   PubMed   Google Scholar  

*Bernstein, G. A., Layne, A. E., Egan, E. A., & Tennison, D. M. (2005). School-based interventions for anxious children. Journal of the American Academy of Child and Adolescent Psychiatry , 44 (11), 1118–1127. https://doi.org/10.1097/01.chi.0000177323.40005.a1 .

Bruhn, A. L., Woods-Groves, S., & Huddle, S. (2014). A preliminary investigation of emotional and behavioral screening in K-12 schools. Education and Treatment of Children , 37 (4), 611–634. https://doi.org/10.1353/etc.2014.0039 .

Article   Google Scholar  

Burns, J. R., & Rapee, R. M. (2021). From barriers to implementation: Advancing universal mental health screening in schools. Journal of Psychologists and Counsellors in Schools , 31 (2), 172–183. https://doi.org/10.1017/jgc.2021.17 .

*Bussing, R., Fernandez, M., Harwood, M., Hou, W., Garvan, C. W., Eyberg, S. M., & Swanson, J. M. (2008). Parent and teacher SNAP-IV ratings of ADHD symptoms: Psychometric properties and normative ratings from a school district sample. Assessment , 15 (3), 317–328. https://doi.org/10.1177/1073191107313888 .

*Caldarella, P., Young, E. L., Richardson, M. J., Young, B. J., & Young, K. R. (2008). Validation of the systematic screening for Behavior disorders in middle and junior high school. Journal of Emotional and Behavioral Disorders , 16 (12), 105–117. https://doi.org/10.1177/1063426607313121 .

Callahan, J. L. (2010). Constructing a manuscript: Distinguishing integrative literature reviews and conceptual and theory articles. Human Resource Development Review , 9 , 300–304. https://doi.org/10.1177/1534484310371492 .

Centers for Disease Control and Prevention (2023). Youth risk behavior surveillance data summary & trends report: 2011–2021 . https://www.cdc.gov/healthyyouth/data/yrbs/pdf/yrbs_data-summary-trends_report2023_508.pdf .

*Chatterji, P., Caffray, C. M., Crowe, M., Freeman, L., & Jensen, P. (2004). Cost assessment of a school-based mental health screening and treatment program in New York City. Mental Health Services Research , 6 (3), 155–166. https://doi.org/10.1023/B:MHSR.0000036489.50470.cb .

Clark, A. G., & Dockweiler, K. A. (2020). Multi-tiered systems of support in Elementary schools: The definitive guide to effective implementation and Quality Control . Routledge.

*Clarke, G. N., Hawkins, W., Murphy, M., Sheeber, L. B., Lewinsohn, P. M., & Seeley, J. R. (1995). Targeted prevention of unipolar depressive disorder in an at-risk sample of high school adolescents: A randomized trial of a group cognitive intervention. Journal of the American Academy of Child and Adolescent Psychiatry , 34 (3), 312–321. https://doi.org/10.1097/00004583-199503000-00016 .

*Cooley-Strickland, M. R., Griffin, R. S., Darney, D., Otte, K., & Ko, J. (2011). Urban African American youth exposed to community violence: A school-based anxiety preventive intervention efficacy study. Journal of Prevention & Intervention in the Community , 39 (2), 149–166. https://doi.org/10.1080/10852352.2011.556573 .

Cree, R. A., Bitsko, R. H., Robinson, L. R., Holbrook, J. R., Danielson, M. L., Smith, C., Kaminski, J. W., Kenney, M. K., & Peacock, G. (2018). Health care, family, and community factors associated with mental, behavioral, and developmental disorders and poverty among children aged 2–8 years—United States, 2016. MMWR Morbidity and Mortality Weekly Report , 67 (50), 1377–1383. https://doi.org/10.15585/mmwr.mm6750a1 .

*Davanzo, P., Kerwin, L., Nikore, V., Esparza, C., Forness, S., & Murrelle, L. (2004). Spanish translation and reliability testing of the child Depression Inventory. Child Psychiatry and Human Development , 35 (1), 75–92. https://doi.org/10.1023/b:chud.0000039321.56041.cd .

*Dierker, L. C., Albano, A. M., Clarke, G. N., Heimberg, R. G., Kendall, P. C., Merikangas, K. R., Lewinsohn, P. M., Offord, D. R., Kessler, R., & Kupfer, D. J. (2001). Screening for anxiety and depression in early adolescence. Journal of the American Academy of Child and Adolescent Psychiatry , 40 (8), 929–936. https://doi.org/10.1097/00004583-200108000-00015 .

*Dowdy, E., Ritchey, K., & Kamphaus, R. W. (2010). School-based screening: A population-based approach to inform and monitor children’s mental health needs. School Mental Health , 2 (4), 166–176. https://doi.org/10.1007/s12310-010-9036-3 .

Dowdy, E., Dever, B. V., Raines, T. C., & Moffa, K. (2016). A preliminary investigation into the added value of multiple gates and informants in universal screening for behavioral and emotional risk. Journal of Applied School Psychology , 32 (2), 178–198. https://doi.org/10.1080/15377903.2016.1165327 .

Duong, M. T., Bruns, E. J., Lee, K., Cox, S., Coifman, J., Mayworm, A., & Lyon, A. R. (2021). Rates of mental health service utilization by children and adolescents in schools and other common service settings: A systematic review and meta-analysis. Administration and Policy in Mental Health , 48 (3), 420–439. https://doi.org/10.1007/s10488-020-01080-9 .

Eijgermans, D. G. M., Fang, Y., Jansen, D. E. M. C., Bramer, W. M., Raat, H., & Jansen, W. (2021). Individual and contextual determinants of children’s and adolescents’ mental health care use: A systematic review. Children and Youth Services Review , 131 , 106288. https://doi.org/10.1016/j.childyouth.2021.106288 .

Elsbach, K. D., & van Knippenber, D. (2020). Creating high-impact literature reviews: An argument for ‘integrative reviews’. Journal of Management Studies , 57 , 1277–1289. https://doi.org/10.1111/joms.12581 .

Fries, D., Carney, K. J., Blackman-Urteaga, L., & Savas, S. A. (2012). Wraparound services: Infusion into secondary schools as a dropout prevention strategy. NASSP Bulletin , 96 , 119–136. https://doi.org/10.1177/0192636512443282 .

*Garrison, C. Z., Jackson, K. L., Addy, C. L., McKeown, R. E., & Waller, J. L. (1991). Suicidal behaviors in young adolescents. American Journal of Epidemiology , 133 (10), 1005–1014. https://doi.org/10.1093/oxfordjournals.aje.a115809 .

Glover, T. A., & Albers, C. A. (2007). Considerations for evaluating universal screening assessments. Journal of School Psychology , 45 , 117–135. https://doi.org/10.1016/j.jsp.2006.05.005 .

*Guo, S., Kim, J. J., Bear, L., & Lau, A. S. (2017). Does depression screening in schools reduce adolescent racial/ethnic disparities in accessing treatment? Journal of Clinical Child and Adolescent Psychology , 46 (4), 523–536. https://doi.org/10.1080/15374416.2016.1270826 .

*Gutierrez, P. M., Watkins, R., & Collura, D. (2004). Suicide risk screening in an urban high school. Suicide & Life-Threatening Behavior , 34 (4), 421–428. https://doi.org/10.1521/suli.34.4.421.53741 .

*Hallfors, D., Brodish, P. H., Khatapoush, S., Sanchez, V., Cho, H., & Steckler, A. (2006). Feasibility of screening adolescents for suicide risk in real-world high school settings. American Journal of Public Health , 96 (2), 282–287. https://doi.org/10.2105/AJPH.2004.057281 .

*Hilt, L. M., Tuschner, R. F., Salentine, C., Torcasso, G., & Nelson, K. R. (2018). Development and initial psychometrics of a school-based screening program to prevent adolescent suicide. Practice Innovations , 3 (1), 1–17. https://doi.org/10.1037/pri0000060 .

*Husky, M. M., McGuire, L., Flynn, L., Chrostowski, C., & Olfson, M. (2009). Correlates of help-seeking behavior among at-risk adolescents. Child Psychiatry and Human Development , 40 (1), 15–24. https://doi.org/10.1007/s10578-008-0107-8 .

*Husky, M. M., Sheridan, M., McGuire, L., & Olfson, M. (2011). Mental health screening and follow-up care in public high schools. Journal of the American Academy of Child & Adolescent Psychiatry , 50 (9), 881–891. https://doi.org/10.1016/j.jaac.2011.05.013 .

*Husky, M. M., Kanter, D. A., McGuire, L., & Olfson, M. (2012). Mental health screening of African American adolescents and facilitated access to care. Community Mental Health Journal , 48 (1), 71–78. https://doi.org/10.1007/s10597-011-9413-x .

Kilgus, S. P., Eklund, K., Maggin, D. M., Taylor, C. N., & Allen, A. N. (2018). The Student Risk Screening Scale: A reliability and validity generalization meta-analysis. Journal of Emotional and Behavioral Disorders , 26 , 143–155. https://doi.org/10.1177/1063426617710207 .

*Klomek, A. B., Kleinman, M., Altschuler, E., Marrocco, F., Amakawa, L., & Gould, M. S. (2013). Suicidal adolescents’ experiences with bullying perpetration and victimization during high school as risk factors for later depression and suicidality. The Journal of Adolescent Health , 53 (1), S37–S42. https://doi.org/10.1016/j.jadohealth.2012.12.008 .

*Laurent, J., Hadler, J. R., & Stark, K. D. (1994). A multiple-stage screening procedure for the identification of childhood anxiety disorders. School Psychology Quarterly , 9 (4), 239–255. https://doi.org/10.1037/h0088291 .

Liu, C., Cox, R. B., Washburn, I. J., Croff, J. M., & Crethar, H. C. (2017). The effects of requiring parental consent for research on adolescents’ risk behaviors: A meta- analysis. Journal of Adolescent Health , 61 (1), 45–52. https://doi.org/10.1016/j.jadohealth.2017.01.015 .

Merikangas, K. R., He, J. P., Burstein, M., Swendsen, J., Avenevoli, S., Case, B., Georgiades, K., Heaton, L., Swanson, S., & Olfson, M. (2011). Service utilization for lifetime mental disorders in U.S. adolescents: Results of the National Comorbidity Survey-Adolescent supplement (NCS-A). Journal of the American Academy of Child and Adolescent Psychiatry , 50 (1), 32–45. https://doi.org/10.1016/j.jaac.2010.10.006 .

Moore, S., Long, A., Coyle, S., Copper, J. M., Mayworm, A. M., Amirazizi, S., Edyburn, K. L., Pannozzo, P., Choe, D., Miller, F. G., Eklund, K., Bohnenkamp, J., Whitcomb, S., Raines, T. C., & Dowdy, E. (2023). A roadmap to equitable school mental health screening. Journal of School Psychology , 96 , 57–74. https://doi.org/10.1016/j.jsp.2022.11.001 .

*Morey, M., Arora, P., & Stark, K. (2015). Multiple stage screening of youth depression in schools. Psychology in the Schools , 52 (8), 800–814. https://doi.org/10.1002/pits.21860 .

Naser, S. C., & Dever, B. V. (2020). A preliminary investigation of the reliability and validity of the BESS-3 teacher and student forms. Journal of Psychoeducational Assessment , 38 (2), 263–269. https://doi.org/10.1177/0734282919837825 .

National Center for School Mental Health (NCSMH). (2020). School mental health quality guide: Screening . NCSMH, University of Maryland School of Medicine.

O’Brien, B. C., Harris, I. B., Beckman, T. J., Reed, D. A., & Cook, D. A. (2014). Standards for reporting qualitative research: A synthesis of recommendations. Academic Medicine: Journal of the Association of American Medical Colleges, 89, 1245–1251 . https://doi.org/10.1097/ACM.0000000000000388 .

Office of the Surgeon General (OSG). (2021). Protecting Youth Mental Health: The U.S. Surgeon General’s Advisory . US Department of Health and Human Services.

*Power, T. J., Costigan, T. E., Leff, S. S., Eiraldi, R. B., & Landau, S. (2001). Assessing ADHD across settings: Contributions of behavioral assessment to categorical decision making. Journal of Clinical Child Psychology , 30 (3), 399–412. https://doi.org/10.1207/S15374424JCCP3003_11 .

Reardon, T., Harvey, K., Baranowska, M., O’Brien, D., Smith, L., & Creswell, C. (2017). What do parents perceive are the barriers and facilitators to accessing psychological treatment for mental health problems in children and adolescents? A systematic review of qualitative and quantitative studies. European Child & Adolescent Psychiatry , 26 , 623–647. https://doi.org/10.1007/s00787-016-0930-6 .

*Riley, A. W., Ensminger, M. E., Green, B., & Kang, M. (1998). Social role functioning by adolescents with psychiatric disorders. Journal of the American Academy of Child and Adolescent Psychiatry , 37 (6), 620–628. https://doi.org/10.1097/00004583-199806000-00012 .

*Rizzo, C. J., Joppa, M. C., Barker, D., Zlotnick, C., Warren, J., Saint-Eloi Cadely, H., & Brown, L. K. (2017). Individual and relationship characteristics of adolescent girls with histories of physical dating violence. Journal of Interpersonal Violence , 35 (5–6), 1389–1414. https://doi.org/10.1177/0886260517696859

*Rowland, A. S., Umbach, D. M., Catoe, K. E., Stallone, L., Long, S., Rabiner, D., Naftel, A. J., Panke, D., Fauly, R., & Sandler, D. P. (2001). Studying the epidemiology of attention-deficit hyperactivity disorder: Screening method and pilot results. Canadian Journal of Psychiatry , 46 (10), 931–940. https://doi.org/10.1177/070674370104601005 .

*Rowland, A. S., Skipper, B. J., Umbach, D. M., Rabiner, D. L., Campbell, R. A., Naftel, A. J., & Sandler, D. P. (2015). The prevalence of ADHD in a population-based sample. Journal of Attention Disorders , 19 (9), 741–754. https://doi.org/10.1177/1087054713513799 .

*Scott, M., Wilcox, H., Huo, Y., Turner, J. B., Fisher, P., & Shaffer, D. (2010). School-based screening for suicide risk: Balancing costs and benefits. American Journal of Public Health , 100 (9), 1648–1652. https://doi.org/10.2105/AJPH.2009.175224 .

Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research , 104 , 333–339. https://doi.org/10.1016/j.jbusres.2019.07.039 .

Soneson, E., Howarth, E., Ford, T., Humphrey, A., Jones, P. B., Coon, J. T., Rogers, M., & Anderson, J. K. (2020). Feasibility of school-based identification of children and adolescents experiencing, or at-risk of developing, mental health difficulties: A systematic review. Prevention Science , 21 (5), 581–603. https://doi.org/10.1007/s11121-020-01095-6 .

Substance Abuse and Mental Health Services Administration (2019). Ready, set, go, review: Screening for behavioral health risk in schools . Rockville, MD: Office of the Chief Medical Officer. https://www.samhsa.gov/sites/default/files/ready-set-go-review-mh-screening-schools.pdf .

Sullivan, J. R., Villarreal, V., Flores, E., Gomez, A., & Warren, B. (2021). SSIS Performance Screening Guide as an indicator of behavior and academics: A meta-analysis. Assessment for Effective Intervention , 46 (3), 228–237. https://doi.org/10.1177/1534508420926584 .

*Sweeney, C., Warner, C. M., Brice, C., Stewart, C., Ryan, J., Loeb, K. L., & McGrath, R. E. (2015). Identification of social anxiety in schools: The utility of a two-step screening process. Contemporary School Psychology , 19 (4), 268–275. https://doi.org/10.1007/s40688-015-0055-9 .

*Torcasso, G., & Hilt, L. M. (2017). Suicide prevention among high school students: Evaluation of a nonrandomized trial of a multi-stage suicide screening program. Child & Youth Care Forum , 46 (1), 35–49. https://doi.org/10.1007/s10566-016-9366-x .

Torraco, R. J. (2005). Writing integrative literature review: Guidelines and examples. Human Resource Development Review , 4 , 356–367. https://doi.org/10.1177/1534484305278283 .

Torraco, R. J. (2016). Writing integrative literature reviews: Using the past and present to explore the future. Human Resource Development Review , 15 , 404–428. https://doi.org/10.1177/1534484316671606 .

van Woudenberg, T. J., Rozendaal, E., & Buijzen, M. (2023). Parents’ perceptions of parental consent procedures for social science research in the school context. International Journal of Social Research Methodology , ahead-of-print , 1–13. https://doi.org/10.1080/13645579.2023.2222539 .

*Vander Stoep, A. V., McCauley, E., Thompson, K. A., Herting, J. R., Kuo, E. S., Stewart, D. G., Anderson, C. A., & Kushner, S. (2005). Universal emotional health screening at the middle school transition. Journal of Emotional and Behavioral Disorders , 13 (4), 213–223. https://doi.org/10.1177/10634266050130040301 .

Villarreal, V. (2018). Mental health referrals: A survey of practicing school psychologists. School Psychology Forum , 12 , 66–77. https://eric.ed.gov/?id=EJ1182049 .

Google Scholar  

Villarreal, V., & Castro-Villarreal, V. (2016). Collaboration with community mental health service providers: A necessity in contemporary schools. Intervention in School and Clinic , 52 , 108–114. https://doi.org/10.1177/1053451216636047 .

Villarreal, V., Castro‐Villarreal, F., Peterson, L., Bear, M., Cortes, D. M., & Escobedo, T. (2023). Meta-Analysis of proportions of students screened and identified in Mental Health Multiple-Gate Screening research. School Psychology Review , 52 (2), 130–143. https://doi.org/10.1080/2372966x.2022.2106155

*Walker, B., Cheney, D., Stage, S., & Blum, C. (2005). Schoolwide screening and positive behavior supports: Identifying and supporting students at risk for school failure. Journal of Positive Behavior Interventions , 7 (4), 194–204. https://doi.org/10.1177/10983007050070040101

Walker, H. M., Small, J. W., Severson, H. H., Seeley, J. R., & Feil, E. G. (2014). Multiple-gating approaches in universal screening within school and community settings. In R. J. Kettler, T. A. Glover, C. A. Albers, & K. A. Feeney-Kettler (Eds.), Universal screening in educational settings: Evidence-based decision making for schools (pp. 47–75). American Psychological Association. https://doi.org/10.1037/14316-003 .

Download references

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. The authors declare no relevant financial interests to disclose.

Author information

Authors and affiliations.

Department of Educational Psychology, University of Texas at San Antonio, 510 W. Cesar E. Chavez Blvd, San Antonio, TX, 78207, USA

Victor Villarreal

Department of Counseling & Educational Psychology, New Mexico State University, Las Cruces, NM, USA

Lisa S. Peterson

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Victor Villarreal .

Ethics declarations

Conflict of interest.

The authors declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Villarreal, V., Peterson, L.S. Mental Health Screening: Recommendations from an Integrated Literature Review. Contemp School Psychol (2024). https://doi.org/10.1007/s40688-024-00501-y

Download citation

Accepted : 21 April 2024

Published : 02 May 2024

DOI : https://doi.org/10.1007/s40688-024-00501-y

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Mental health
• Multiple stage
• Find a journal
• Publish with us
• Track your research

Service design for children and young people with common mental health problems: literature review, service mapping and collective case study

Affiliations.

• 1 School of Health Sciences, The University of Manchester and Manchester Academic Health Science Centre (MAHSC), Manchester, UK.
• 2 Greater Manchester Mental Health NHS Foundation Trust, Manchester, UK.
• 3 School of Healthcare Studies, Cardiff University, Cardiff, UK.
• 4 Common Room North, Leeds, UK.
• 5 Lancashire and South Cumbria NHS Foundation Trust, Preston, UK.
• PMID: 38767587
• DOI: 10.3310/DKRT6293

Background: The mental health of children/young people is a growing concern internationally. Numerous reports and reviews have consistently described United Kingdom children's mental health services as fragmented, variable, inaccessible and lacking an evidence base. Little is known about the effectiveness of, and implementation complexities associated with, service models for children/young people experiencing 'common' mental health problems like anxiety, depression, attention deficit hyperactivity disorder and self-harm.

Aim: To develop a model for high-quality service design for children/young people experiencing common mental health problems by identifying available services, barriers and enablers to access, and the effectiveness, cost effectiveness and acceptability of such services.

Design: Evidence syntheses with primary research, using a sequential, mixed-methods design. Inter-related scoping and integrative reviews were conducted alongside a map of relevant services across England and Wales, followed by a collective case study of English and Welsh services.

Setting: Global (systematic reviews); England and Wales (service map; case study).

Data sources: Literature reviews: relevant bibliographic databases and grey literature. Service map: online survey and offline desk research. Case study: 108 participants (41 children/young people, 26 parents, 41 staff) across nine case study sites.

Methods: A single literature search informed both reviews. The service map was obtained from an online survey and internet searches. Case study sites were sampled from the service map; because of coronavirus disease 2019, case study data were collected remotely. 'Young co-researchers' assisted with case study data collection. The integrative review and case study data were synthesised using the 'weaving' approach of 'integration through narrative'.

Results: A service model typology was derived from the scoping review. The integrative review found effectiveness evidence for collaborative care, outreach approaches, brief intervention services and the 'availability, responsiveness and continuity' framework. There was cost-effectiveness evidence only for collaborative care. No service model appeared to be more acceptable than others. The service map identified 154 English and Welsh services. Three themes emerged from the case study data: 'pathways to support'; 'service engagement'; and 'learning and understanding'. The integrative review and case study data were synthesised into a coproduced model of high-quality service provision for children/young people experiencing common mental health problems.

Limitations: Defining 'service model' was a challenge. Some service initiatives were too new to have filtered through into the literature or service map. Coronavirus disease 2019 brought about a surge in remote/digital services which were under-represented in the literature. A dearth of relevant studies meant few cost-effectiveness conclusions could be drawn.

Conclusions: There was no strong evidence to suggest any existing service model was better than another. Instead, we developed a coproduced, evidence-based model that incorporates the fundamental components necessary for high-quality children's mental health services and which has utility for policy, practice and research.

Future work: Future work should focus on: the potential of our model to assist in designing, delivering and auditing children's mental health services; reasons for non-engagement in services; the cost effectiveness of different approaches in children's mental health; the advantages/disadvantages of digital/remote platforms in delivering services; understanding how and what the statutory sector might learn from the non-statutory sector regarding choice, personalisation and flexibility.

Study registration: This study is registered as PROSPERO CRD42018106219.

Funding: This award was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme (NIHR award ref: 17/09/08) and is published in full in Health and Social Care Delivery Research ; Vol. 12, No. 13. See the NIHR Funding and Awards website for further award information.

Keywords: ADOLESCENT; ADOLESCENT HEALTH SERVICES; CAMHS; CASE STUDY RESEARCH; CHILD; CHILD HEALTH SERVICES; DELIVERY OF HEALTH CARE; HEALTH SERVICES; INTEGRATIVE REVIEW; LITERATURE REVIEW; MENTAL DISORDERS; MENTAL HEALTH; MENTAL HEALTH SERVICES; ORGANISATIONAL CASE STUDIES; SCHOOL MENTAL HEALTH SERVICES; SCOPING REVIEW; SERVICE MAP; SERVICE MODEL; SYSTEMATIC REVIEW; TYPOLOGY.

Plain language summary

In this research study, we explored services for children and young people with ‘common’ mental health problems like depression, anxiety and self-harm. We aimed to find out what services exist, how children/young people and families find out about and access these services, what the services actually do, whether they are helpful and whether they offer value for money. We looked at the international literature (reports and research papers) to identify different approaches to providing support, and to find out whether certain approaches worked better than others and whether children/young people and families preferred some approaches over others. The literature provided very little information about the value for money of services. We also carried out a survey and used the internet to identify 154 relevant services in England and Wales. To explore services in more detail, and hear directly from those using them, we planned to visit 9 of the 154 services to interview children/young people, parents and staff. Unfortunately, coronavirus disease 2019 stopped us directly visiting the nine services and so we conducted phone and video interviews instead. We still managed to speak to, and hear the experiences of, more than 100 people (including children/young people and parents). We combined information from the literature with information from the interviews to create an evidence-based ‘model’ of what services should look like. This model considers some basic things like how quickly children/young people could access a service, what information was available, the importance of confidentiality and whether staff make the service fit with the child/young person’s needs and interests. It also considers whether the service helps children/young people learn skills to manage their mental health and whether staff at a service work well together. We hope our model will help existing and new services improve what they offer to children/young people and families.

Publication types

• COVID-19 / epidemiology
• Child Health Services / organization & administration
• Cost-Benefit Analysis
• Health Services Accessibility / organization & administration
• Mental Disorders* / therapy
• Mental Health Services* / organization & administration

• Career Center
• Member Login

AcademyHealth Presents a Literature Review Summarizing the Research Findings of the Use of Internet Search Data in the Diagnosis of Diseases and Conditions

Sponsored by a grant from the Gordon and Betty Moore Foundation, and in partnership with Innovation Horizons, AcademyHealth conducted a literature review on the use of patient-generated data to assess the capability of the internet as a tool to inform clinical diagnosis practices.

As a component of an ongoing research project to address the gaps in medical care that contribute to delayed or missed diagnosis of serious disease and conditions, AcademyHealth has engaged in a study to examine how the use of a patient’s internet search data can be applied as a data source and tool to inform clinical diagnosis practices. Sponsored by a grant from the Gordon and Betty Moore Foundation, a literature review and comparative analyses were conducted utilizing data from internet searches by patients; the purpose of the review and analyses was to identify correlations (or a lack thereof) between each patient's searches and the corresponding patient's medical diagnoses. While many scientific reviews have previously summarized the utility of population-level internet search data for public health and health outcomes research, this is the first documented assessment of peer-reviewed publications focused on personalized medical diagnostic use. AcademyHealth conducted this study to help guide health services researchers, patient advocates, health care policy makers, and others to learn more about these data resources and their potential to inform new approaches to address diagnostic gaps and opportunities. This approach applies patient-generated data to assess the capability of the internet as a tool to improve understanding of symptoms, illuminate warning signs leading to a medical diagnostic workup, and empower caregivers to more effectively respond to health concerns.

The study design and findings represented in this report were reviewed by an Academy Health multidisciplinary steering committee that approved the report. The report details how researchers obtained and analyzed patient data from Microsoft and Google search engines to then link the data with the corresponding patient’s clinical information. All patient-generated data was provided by patients retrospectively with informed consent for research use. Of the 43 pee-reviewed publications identified and reviewed by AcademyHealth for inclusion in the report, all were retrospective analyses. The analyses included in the report represented a variety of medical and health considerations including cancer, mental health conditions, vulnerability, violence, and personal safety associated with neurodegenerative and aging disorders. Also included in the report were features published by researchers about tools that were developed to enable large data sets to be analyzed and integrated with other data, such as clinical trials and clinical care data. Cumulatively, these publications provide unique insights into the patient information needs, informed consent, and clinical research overview requirements needed to meet standards of conduct for research involving human subjects. 

While many of these research publications identified signals of potential clinical use in diagnosis for specific diseases and conditions, none of the studies offered the type of confirmatory evidence that are required for medical use; at a minimum, evidence required would include prospective randomized studies. Therefore, this nascent type of diagnostic application research using large patient internet search data sets needs additional sponsorship to support infrastructure, analytic approaches, and importantly, innovative study design that enables prospective data collections to avoid bias and ultimately better understand the clinical efficacy and utility of this practice. 

Further information can be found at the Exploring Consumer & Patient Internet Search Data to Improve Diagnosis Grant Program webpage . Innovation Horizons provided the research and analysis with support from the Gordon and Betty Moore Foundation. 

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Front Public Health

Promoting University Students' Mental Health: A Systematic Literature Review Introducing the 4M-Model of Individual-Level Interventions

Bhavana nair.

1 Guidance & Counseling Office, Student Services & Registration, Mohammed Bin Rashid University of Medicine and Health Sciences (MBRU), Dubai, United Arab Emirates

Farah Otaki

2 Strategy & Institutional Excellence, Mohammed Bin Rashid University of Medicine and Health Sciences (MBRU), Dubai, United Arab Emirates

Associated Data

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.

Objective: The purpose of this study is to systematically review recently published individual student-level interventions aimed at alleviating the burden of mental health challenges faced by the students and/ or at equipping them with coping mechanism that will foster their resilience.

Methods: This study relied on a systematic literature review. PubMed dataset was used; the search was confined to the following period: July 2016-December 2020.

Results: A total of 1,399 records were identified by the electronic search, out of which 40 studies were included in this study. The authors inductively identified four overlapping categories of interventions across all included articles, and coded them as follows: Mindfulness, Movement, Meaning, and Moderator. Accordingly, each study was linked to at least one of four overlapping categories based on the nature of the intervention(s) under investigation, leading to differing assortments of categories.

Conclusions: The 4M-Model generated by this study encourages focusing on devising holistic, university-based interventions that embrace the individuality of students to improve their mental health through elements of mindfulness, movement, meaning, and moderator. Through this focused approach, university counselors are enabled to design interventions that address students' physical, psychological, emotional, and social needs.

Introduction

There has been a positive paradigm shift in the way our world and its citizens are perceiving the concept of mental health. Mental health is a state of well-being that allows individuals to enjoy and maintain relationships as well as handle stress in a healthy manner without compromising on productivity ( 1 ).

A large body of literature on tertiary education students highlights the importance of maintaining mental health with evidence relating it to educational attainment and productivity ( 2 ), social relationships, engagement on campus, and quality of life ( 3 ), and placement performance ( 4 ). Poor mental health has also been linked with lower retention within a programme, grade point averages, and graduation rates among university students ( 5 ). Counseling, psychoeducation, and mental health services on campuses are no longer deemed as merely supportive but rather an integral component necessary to empower students. These services are integral to help students develop skills such as psychological flexibility ( 6 ) which in turn influences mental health ( 1 ).

The current generation of university students is vastly different from previous generations, especially in their attitudes and beliefs toward their mental health needs. Well-being is a dynamic concept of interlinked physical, social, and psychological dimensions which is constantly changing depending on intrinsic and extrinsic environments and motivations ( 7 ). It is not only the demographics of the current generation of university students that has changed considerably from the past ( 8 ), but so have their attitudes and beliefs toward their needs, including mental health ( 3 ). This population is considered high risk because most mental health problems are triggered before the age of 24 ( 9 ). There is enough evidence to link personal and academic stressors to mental health ( 10 – 12 ). Contemporary tertiary education is striving to attain and maintain cultures of excellence, similar to traditional universities in the past ( 13 ). However, there has been a shift to turn modern day campuses into high stakes competitive testing environments with well-intended emphasis on preparing students to become part of the global economy. This change has influenced the context in which modern universities function. There are a set of challenges that contemporary universities face that extend beyond the earlier tertiary educational institutions and there is an assumption that students are coming to college “overwhelmed and more damaged than those of previous years” ( 14 ).

Although good citizenship has always been an important foundation of all educational institutions, with the dynamic social landscape that the universities are set within, there seems to be a tendency to lead students to fixate on extrinsic factors such as: results and Grade Point Averages, over intrinsic interest such as innovative learning, and expansion of lateral thinking ( 13 ). When the priority is grades, it manifests itself in excessive hours of focused studying, and in negative coping behaviors, such as: inadequate sleep and addictive behaviors, which could potentially affect the well-being of the student. Often, in this pursuit of academic excellence, there is the danger of ignoring the social, emotional, and psychological problems that modern students are now increasingly facing.

There is enough research that indicates that students are experiencing more mental health disorders in contemporary times and are less resilient than students in the past ( 8 ), with lower levels of frustration tolerance ( 15 ). Anxiety and depression are most prevalent among tertiary students ( 16 ). There is a rise in the number of college students with a diagnosable psychological disorder ( 17 ) with some students at greater risk than others of experiencing stress and mental health problems ( 18 ). There has been also a shift in the severity of the problems by students seeking counseling services over the past decade. It is no longer just presenting challenges of adjustment and individuation ( 19 ), or benign hormonal developmental problems associated with the age that prompts students to seek counseling. Students are presenting with severe psychological problems ( 20 ) with a sizeable number of them on psychiatric medication to help them function better on campus ( 15 ).

A common narrative through an exhaustive body of literature highlights the barriers to seeking help for mental health problems by students on campus due to stigma ( 21 ), scepticism about treatment efficacy ( 22 ), and a belief that their emotional problems will not be completely understood. This leads to a sense of social isolation as the students restrain from reaching out for help ( 21 ). Two contributing factors to inadequate help-seeking are the stigma of having a mental health problem and the personal characteristics of the individual student ( 20 ). A fear of negative consequences on academic records ( 23 ) is another common barrier among university students. Interestingly, students resist seeking help because they do not perceive their condition to require intervention or do not perceive it as a priority among their other commitments. They also have the tendency to normalize stress as part of university life, expecting it “will go away with time,” and prefer to handle their problems on their own ( 24 ).

More recent research indicates that students also rely on informal sources of help-seeking from non-professionals, particularly peer groups ( 25 ). Students report having no inhibitions about having open discussions about their mental health problems via social-networking websites ( 26 ). This resonates with the network episode model of help-seeking that emphasizes the social network as an integral, contemporary support in enhancing knowledge and attitudes toward seeking help ( 27 ). However, there is also a significant increase in the number of students with major psychological problems seeking counseling services on campus ( 3 ) challenging the stigma connected with help-seeking. The newer generation's familiarity with psychosocial support services and openness toward seeking them are putting mental health at the core of self-care, much like diet and exercise ( 26 ).

Along with rapid social changes and expectations, the dilution of traditional family anchors (that is the changes to family systems which include busy yet isolated lifestyles, social media pressures, a living free from parental influence which is very common to this age group, and forced separation from families in the pursuit of dream destinations for education) all compounding to the considerable degree of stress that students report upon ( 18 ). Considering all these transitions, focusing on the support that is available to young people on campus is increasingly becoming a necessity. This is not only a personal benefit for students but a national and international investment that could also result in considerable economic benefit ( 28 ) as these students stand to become contributors to the global economy.

A wealth of research exists which highlights the effectiveness of changing organizational factors that influence mental health ( 29 , 30 ). However, there is limited research on person-centric mental health strategies used in university settings ( 31 ). A Systematic Literature Review that was conducted by Fernandez et al. focused on evaluating the effect of setting-based interventions that stimulated and improved the mental health and well-being of university students and employees ( 32 ). That review constitutes an asset for universities seeking to adopt setting-based strategies that were proven efficacious. Yet, given the highspeed in which the higher education ecosystem has been evolving, there is an evident need for a more up-to-date review. Also, despite the importance of modifying the environment for it to become more nurturing for university students' mental health, this needs to be in conjunction with embracing the individuality of each student. Accordingly, the purpose of this study is to bridge this gap through providing a review of the literature on recently published individual student-level interventions that aim to alleviate the burden of mental health challenges faced by the students and/or help them with coping mechanisms that will foster their resilience.

We conducted a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines ( 33 ). The protocol of the systematic review was published in PROSPERO, a database of prospectively registered systematic reviews in health and social care (CRD42021227862).

Search Strategy

To complement the work of Fernandez et al., focusing on the recent literature, the search period was confined to July 2016 through December 2020 ( 32 ). PubMed database was used. The search strategy used, with its key words and Boolean logic, is available as an online resource. It was structured as follows:

• Subjects: student or resident.
• Location: higher education, university, college, or tertiary education.
• State-of-being : mental health.
• Challenges faced by subjects : psychosocial, anxiety, depression, burnout, stress, peer-pressure, social media pressure, bullying, eating disorder, perfectionism, or learning difficulties.
• Intervention to address the challenges : psychotherapy, mindfulness, Counseling, support group, yoga, breathing, art therapy, awareness, resilience, gratitude, affirmations, or peer-Counseling.

Pure qualitative studies were excluded. We included all quantitative studies, so long as they contained information on the impact of the intervention. These included those using experimental (i.e., randomized controlled trials) or observational (i.e., controlled trials without randomization, and pre-post and time series) approaches. Duplicated papers were excluded. Studies were screened for inclusion in three phases:

• BN and FO went over all the abstracts, together, to remove the articles that certainly did not meet the inclusion criteria.
• The full text of all the remaining abstracts were reviewed independently by BN and FO. The results were discussed. Any discrepancies were investigated and reflected upon until reaching consensus.
• Finally, all remaining articles were thoroughly reviewed for summarizing purposes based on a preset template: research study objective, context, design, method, sample, intervention, and main conclusion.

Articles were included if:

• a) Empirical/applied (i.e., theoretical studies or systematic reviews, and studies using secondary data were excluded),
• b) Conducted in one or more university,
• c) Aimed at evaluating, the immediate or long-term effect of an intervention on the mental health status of students,
• d) Included global measures of mental health and well-being,
• e) Had the university counselor involved in the intervention,
• f) Involved full-time students, and
• g) Was written in English.

Quality Assessment

The quality of each of the included articles was evaluated considering the internal and external validity. For the internal validity (risk of bias), each study's methodological quality was assessed using the criteria introduced by Jadad et al. ( 34 ). As for the external/ ecological validity of the included studies, it was assessed using the criteria developed by Green and Glasgow ( 35 ). This quality assessment was not used to exclude articles. Yet, the results of the assessment were thoroughly reflected upon as an evaluative measure of the review output.

Data Analysis

The interventions referred to in the included studies were analyzed by the researchers using the framework of Braun and Clarke ( 36 ). The intention was to inductively build a general interpretation of all included studies, in alignment with the paradigm of constructivism ( 37 , 38 ). The assumption was that reality is socially-constructed. This required thoroughly reflecting upon the interventions investigated in the included studies. The process of exploratory reflection adapted was spiral, where the researchers' observations kept getting revisited which culminated into the development of an evidence-driven model. Since the constructivism paradigm gives precedence to thoroughness and insightfulness over extensiveness and generalizability ( 39 ), the decision was made upfront, as abovementioned, for this search to be limited to a single database ( 40 ). As for the purpose of the qualitative meta-synthesis, it was to create a dynamic individual-level intervention framework that is holistic and context-specific ( 41 ). All articles were categorized based on the nature of the intervention(s) under investigation. It is all narratively presented in the results section.

A total of 1,399 records were identified by the electronic search. Two researchers (BN and FO) reviewed all the abstracts of the resulting papers to identify ones that fitted the inclusion criteria. Based on that, a total of 1,178 articles were excluded. The full text of all remaining 220 articles were extracted and thoroughly reviewed by the two researchers (110 by each). Accordingly, 133 articles were excluded. The remaining 87 articles underwent another round of assessment by both researchers together. Out of these 87 articles, 47 papers were excluded: four studies did not meet the eligibility criteria of having an intervention in them, 31 studies did not include assessing the effectiveness of an intervention,10 studies were not exclusively on university students, and 1 was not on full-time students. Also, one study was excluded because it was not counselor-led but outsourced. Out of the initially identified 1399 articles, 40 articles were finally included in the study ( Figure 1 ).

PRISMA flow-diagram. Promoting university students' mental health: a systematic literature review introducing the 4M-Model of individual-level interventions, Dubai, United Arab Emirates, 2020.

Of the 40 studies, nine studies were conducted in USA, eight in United Kingdom, four in Canada, three in Australia, five in Germany, four in China, and one in each of Turkey, Hungary, Israel, Ireland, Japan, South Korea and Netherlands. The quality of evidence is very high in terms of internal validity because most of the studies ( 25 ) employed RCT, five studies used a quasi-experimental method, two had a cross sectional design, and eight studies utilized a pre-post design without a control group.

The external validity of the papers could be considered low/ moderate. Since most of the studies indicated the experience of only one institution; generalization of the findings is limited. The only exceptions were one study that was conducted in Israel which included three institutions and one conducted in UK which included eight universities. After thoroughly reflecting upon the interventions under investigation across all 40 resulting studies, the authors qualitatively synthesized a holistic framework. This involved inductively identifying four overlapping categories of interventions. Each category was in turn coded with a label that appeared to be most fit to the encapsulated interventions and that is in harmony with the codes of the rest of the categories (i.e., alliteration).

Accordingly, each study was linked to at least one of four overlapping categories based on the nature of the intervention(s) under investigation ( Table 1 ). The first category, coded as Mindfulness, included individual-level interventions that used mindfulness as a strategy to promote mental health. Mindfulness, in this context, refers to any intervention that aims to promote living in the moment or “now” and adopting acceptance and a non-judgmental attitude to guide action. The popular Mindfulness Based Stress Reduction (MBSR) curriculum was used in four studies ( 8 , 42 – 45 ). Mindfulness Based Cognitive Therapy (MBCT) which focuses on reframing thoughts along with becoming aware of the nature and quality of them was found to also be effective in two studies ( 46 , 47 ). In three studies, the intervention(s) made use of imagery and self-guidance ( 48 – 51 ), whereas two other studies explored the effectiveness of Acceptance and Commitment Therapy (ACT) ( 6 ) to improve the psychological flexibility, school engagement, and mental health among University students.

Distribution of the output of the systematic literature review depending on the nature of the intervention(s) under investigation.

Promoting university students' mental health: a systematic literature review introducing the 4M-Model of individual-level interventions, Dubai, United Arab Emirates, 2020 .

The second category of studies was coded as Movement and included individual-level interventions which have a predominant physical element and solicit change in bodily sensations including but not limited to yoga, fitness, dance, kickboxing, and aerobics and breathing exercises. While Tong et al. ( 52 ) exclusively looked at the effect of Yoga and Fitness on mental health, five sets of researchers ( 8 , 42 , 43 , 45 , 46 ) looked at breathing and simple yoga as part of their mindfulness course. Sleep was studied in connection to mental health in two studies ( 53 , 54 ) as it has been found to be a precursor to many mental health problems with insomnia and the quality of sleep put on top of the list affecting sleep hygiene. Behavioral activation, a personalized therapeutic tool mainly used in the treatment of depression targeting behaviors that feed into the condition, was found to be effective in three studies that were reviewed ( 55 – 57 ) involving students with mild depression. The goal of Behavioral Activation is engaging in enjoyable activities with a part of the process focusing on getting past obstacles that may impede that enjoyment. One study included peer-led support ( 56 ) and online delivery of the course ( 57 ), where both appeared to be efficacious. Only one study by Chalo et al. ( 58 ) used Biofeedback intervention, that involved measuring students' quantifiable bodily functions to convey information to them in real-time as a solution to help students manage their physiological response to anxiety and stress.

The third category was coded as Meaning and included studies that investigate individual-level interventions that focus on the counselor addressing connections and associations between variables and enabling the student to reframe cognitions. Psychoeducation was widely utilized with cognitive training as the most common ( 54 , 59 – 63 ). Eustis et al. ( 49 ) focused their study on the student's self-awareness, while Demir and Ercan ( 64 ) explored communication techniques among students. In addition, three studies explored the feasibility of having courses embedded within the curriculum ( 38 , 48 , 50 ) to improve the mental health of students, while nine studies explored the effect of elective courses that aimed at stress reduction ( 18 , 43 , 50 , 56 , 58 , 65 – 69 ).

The last category of studies was coded as Moderator which referred to any element of support that was deployed in conjunction with the counselor, in an individual-level intervention, that acts as a moderator between the student and the counselor. Pet therapy was explored in three studies ( 70 – 72 ) to assess well-being, and an extensive use of the computer to deliver courses such as ACT, Psychoeducation, and Cognitive Behavior Therapy (CBT) which are all traditionally effective in psychotherapy, were found to be efficacious online in 10 studies ( 44 , 50 , 57 , 61 , 73 – 78 ) highlighting the significance of the potential of web-based interventions to impart psychotherapy to a wider audience.

This literature review showed that elements of Mindfulness were a major part of the 23 studies, Meaning was predominant in 24 studies, while Movement was an important feature in 17 studies. An element of support complementary to the therapist, either in the form of a pet (canine) or a web/phone application (i.e., Moderator), was part of 16 interventions. Commonly used approaches were Mindfulness based therapies, ACT, Cognitive Behavior Therapy, and Psychoeducation. The duration of the interventions investigated in the included studies ranged between 1 and 12 weeks, with most of the studies spanning between 6 and 8 weeks. Nine studies had just one element, and only one study ( 49 ) had all the four elements included ( Figure 2 ), which the authors perceived as a “lucky find.”

The 4M-Model generated from this study's qualitative synthesis, visually illustrated as a four-leaf clover which is a symbol of luck. Promoting university students' mental health: a systematic literature review introducing the 4M-Model of individual-level interventions, Dubai, United Arab Emirates, 2020.

Thirty-one studies had overlapping elements indicating that these elements are not mutually exclusive and rather interlinked and are blended with the intention of enhancing the effectiveness of a program.

The output of this Systematic Literature Review revealed diverse interventions. Most of these interventions were hybrid versions of existing evidence-based interventions. A few of the identified articles reflected upon contextualized home-grown interventions. There appeared to be a lack of consensus on a common model/ approach to effectively improve the mental health and wellness of university students ( 61 ) who are known to have their own set of challenges. Hence, this paper provides an outline of practices that have been deployed in this direction, illustrating them from a holistic perspective. Elements of mindfulness, meaning, movement, and use of a moderator were seen to overlap in the studies. The blending of these elements was proven to be effective in improving metacognitive awareness, emotional regulation ( 79 ), concentration, and mental clarity ( 80 ), and decreasing emotional reactivity ( 81 ) and rumination (through disengagement with persistent negative thoughts) ( 82 ) and in turn reducing depression, stress, and anxiety ( 83 ). It has also shown to foster social connectedness and the ability to express oneself in various social situations ( 84 ) thereby reducing stress and anxiety and increasing patience, gratitude, and body awareness ( 85 ). With so many elements that need to be taken into consideration, the researchers have attempted to comprehend the output of this review from the field theory point-of-view where the “organism and environment are perceived as part of an interacting field” ( 86 ).

Moreover, Counseling strategies and interventions are meant to emphasize on the growth of an individual. The human potential for self-actualization, a concept understood by Abraham Maslow as a change process that aims at making a person “aware of what is going on inside himself” [Maslow, as cited in Seaman ( 87 ), p. 3] is core to Counseling interventions, which is where the four elements blend to become crucial to the process of self-awareness and eventually self-growth.

The results of the study indicate that self-awareness through mindfulness is an important foundation upon which all other elements build up to improve mental health of students. This was not a surprising find because this is in alignment with the results of many previously conducted studies ( 88 , 89 ). Mindfulness seems to be the new mantra and has been intensively researched ( 90 ). However, despite a substantial amount of theoretical work conducted to merge Buddhist and Western conceptual viewpoints to psychotherapy ( 91 ), there is minimal literature on how it can translate to practice making this review an important addition to the limited knowledge around the topic of psychological interventions that have been found to be effective among university students. MBSR has proven to reduce stress and anxiety among university students by fostering insight and concentration along with physiologic relaxation ( 92 ). Teaching students to live in the present moment by reframing thoughts (i.e., MBCT) has been found to be effective in reducing depression ( 93 ). It also lessens the risk of relapse with comparable efficacy to antidepressant medication ( 94 ) which, in itself, is a breakthrough for psychotherapy. ACT which focuses on acceptance has been found to improve coping, self-regulation, psychological flexibility, and school engagement ( 6 ). Counseling young adults, in particular students at the university level, would benefit by basing it on Engel's biopsychosocial viewpoint which includes taking into consideration the hormonal changes (biological), identity crisis, and the challenges arising from intimacy and isolation (psychological) which have been hypothesized in Eric Erickson's psychosocial stages of development for this age group. The new age technological challenges of peer-pressure over social media sites and the demands of fitting in and changing family dynamics (sociological) also need to be taken into consideration when conceptualizing a Counseling program for this target group.

Moreover, this transition stage between adolescence and adulthood, also referred to as “emerging adulthood” ( 95 ), is considered to be a period of accepting responsibility for one's actions and livelihood, developing belief systems and values independent of parental and external influences, and establishing relationships with parents on equal grounds. Young university students who are still financially dependent and living with parents during this period are arbitrarily considered to be adolescents if adult responsibilities are not yet accessed. These intangible markers gradually develop. The entailed process could last many years until the corresponding responsibilities are effectively adopted. As such, the range between adolescence and adulthood becomes wider than typically defined, stretching from the beginning of puberty to the early twenties ( 96 ).

Counseling has been traditionally associated as a profession that requires the physical presence of a minimum of two people in a professional relationship to talk through and process experiences to gain insight and understanding. However, in this review, it is evident that web-based interventions seem to produce an equally effective result ( 97 ) as observed in 16 studies of the literature review which could be utilized as a complementary medium widening the scope of practice of counselors and psychotherapists. This could also help in minimizing the stigma associated with getting undesirably labeled and help in reducing psychological self-restraint which has been termed as ‘online disinhibition effect' ( 98 ). Web-based mental health interventions also are becoming a preferred medium for students to gain services and information ( 99 ) as they accommodate their busy schedules ( 100 ).

Another observation was that even though most of the interventions were conducted only for a short period of time, the effectiveness of the interventions was established. Embedding interventions within the curriculum has been suggested ( 101 ) which makes this review even more pertinent for innovations in curriculum planning. This may also help in alleviating the stigma that is attached to Counseling services which is often a barrier that prevents students from reaching out for help ( 102 ). This aligns with Vygotsky's notion of Zone of Proximal Development ( 103 ) which refers to pedagogical support being beneficial for activities, in this context, psychoeducation of positive behaviors that facilitate help seeking behaviors before they can start using them independently.

The above observations prompted the researchers to recognize that the four identified elements when combined would result in a holistic approach of addressing the individual from a biopsychosocial point-of-view. This was depicted in the form of the 4M-Model to guide counselors to develop and implement university-level interventions that could help to reduce stress, anxiety, and depression as well as improve emotion regulation and self-awareness to address the mental health needs of young adults. It would be worthwhile for future research studies to validate the suggested 4M-Model through a similar systematic review of the literature relying on a combination of databases ( 104 ). The analysis in this case would be deductive where the model conceived from this study can be used as a preset template. Also, for validation purposes, it is recommended to conduct follow-up studies aimed at evaluating the efficaciousness of a tailor-made assortment of interventions that can be linked to all elements of the 4M-Model. For that purpose, it would be useful to adapt a mixed methods approach to research, where quantitative and qualitative findings will be integrated to obtain a holistic perspective of the output, outcome, and impact of such university-based, individual-student level mental health initiatives.

Findings of this review reveal the 4M-Model that happen to address all aspects of holistic well-being: physical, psychological, emotional, and social. Effectiveness of the varied interventions that have been reviewed in this study indicate that if a comprehensive approach toward intervention including mindfulness, movement, moderator, and meaning is adapted, then it would not only help students to be supported in a holistic manner but would help counselors plan and execute their programs in a focused approach to address the needs of any university student population who are increasingly overwhelmed and burned out with the stressors from their outside worlds as well as from within. The findings from the review add to the growing evidence for the urgent need of an intervention model that can serve as a directive for counselors and students.

Data Availability Statement

Author contributions.

BN and FO conceptualized the study, conducted the review, performed the qualitative meta-synthesis, and prepared and approved the manuscript. Both authors contributed to the article and approved the submitted version.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

The authors would like to extensd their gratitude to three of their colleagues: Dr. Lisa Jackson, Dr. Leigh Powell, and Ms. Mersiha Kovacevic, for their active role, and valuable reflections and feedback in reviewing the complete manuscript.