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A hypothesis test is a procedure used in statistics to assess whether a particular viewpoint is likely to be true. They follow a strict protocol, and they generate a ‘ p- value’, on the basis of which a decision is made about the truth of the hypothesis under investigation. All of the routine statistical ‘tests’ used in research— t- tests, χ 2 tests, Mann–Whitney tests, etc.—are all hypothesis tests, and in spite of their differences they are all used in essentially the same way. But why do we use them at all?
Comparing the heights of two individuals is easy: we can measure their height in a standardised way and compare them. When we want to compare the heights of two small well-defined groups (for example two groups of children), we need to use a summary statistic that we can calculate for each group. Such summaries (means, medians, etc.) form the basis of descriptive statistics, and are well described elsewhere. 1 However, a problem arises when we try to compare very large groups or populations: it may be impractical or even impossible to take a measurement from everyone in the population, and by the time you do so, the population itself will have changed. A similar problem arises when we try to describe the effects of drugs—for example by how much on average does a particular vasopressor increase MAP?
To solve this problem, we use random samples to estimate values for populations. By convention, the values we calculate from samples are referred to as statistics and denoted by Latin letters ( x ¯ for sample mean; SD for sample standard deviation) while the unknown population values are called parameters , and denoted by Greek letters (μ for population mean, σ for population standard deviation).
Inferential statistics describes the methods we use to estimate population parameters from random samples; how we can quantify the level of inaccuracy in a sample statistic; and how we can go on to use these estimates to compare populations.
There are many reasons why a sample may give an inaccurate picture of the population it represents: it may be biased, it may not be big enough, and it may not be truly random. However, even if we have been careful to avoid these pitfalls, there is an inherent difference between the sample and the population at large. To illustrate this, let us imagine that the actual average height of males in London is 174 cm. If I were to sample 100 male Londoners and take a mean of their heights, I would be very unlikely to get exactly 174 cm. Furthermore, if somebody else were to perform the same exercise, it would be unlikely that they would get the same answer as I did. The sample mean is different each time it is taken, and the way it differs from the actual mean of the population is described by the standard error of the mean (standard error, or SEM ). The standard error is larger if there is a lot of variation in the population, and becomes smaller as the sample size increases. It is calculated thus:
where SD is the sample standard deviation, and n is the sample size.
As errors are normally distributed, we can use this to estimate a 95% confidence interval on our sample mean as follows:
We can interpret this as meaning ‘We are 95% confident that the actual mean is within this range.’
Some confusion arises at this point between the SD and the standard error. The SD is a measure of variation in the sample. The range x ¯ ± ( 1.96 × SD ) will normally contain 95% of all your data. It can be used to illustrate the spread of the data and shows what values are likely. In contrast, standard error tells you about the precision of the mean and is used to calculate confidence intervals.
One straightforward way to compare two samples is to use confidence intervals. If we calculate the mean height of two groups and find that the 95% confidence intervals do not overlap, this can be taken as evidence of a difference between the two means. This method of statistical inference is reasonably intuitive and can be used in many situations. 2 Many journals, however, prefer to report inferential statistics using p -values.
In 1925, the British statistician R.A. Fisher described a technique for comparing groups using a null hypothesis , a method which has dominated statistical comparison ever since. The technique itself is rather straightforward, but often gets lost in the mechanics of how it is done. To illustrate, imagine we want to compare the HR of two different groups of people. We take a random sample from each group, which we call our data. Then:
Formally, we can define a p- value as ‘the probability of finding the observed result or a more extreme result, if the null hypothesis were true.’ Standard practice is to set a cut-off at p <0.05 (this cut-off is termed the alpha value). If the null hypothesis were true, a result such as this would only occur 5% of the time or less; this in turn would indicate that the null hypothesis itself is unlikely. Fisher described the process as follows: ‘Set a low standard of significance at the 5 per cent point, and ignore entirely all results which fail to reach this level. A scientific fact should be regarded as experimentally established only if a properly designed experiment rarely fails to give this level of significance.’ 3 This probably remains the most succinct description of the procedure.
A question which often arises at this point is ‘Why do we use a null hypothesis?’ The simple answer is that it is easy: we can readily describe what we would expect of our data under a null hypothesis, we know how data would behave, and we can readily work out the probability of getting the result that we did. It therefore makes a very simple starting point for our probability assessment. All probabilities require a set of starting conditions, in much the same way that measuring the distance to London needs a starting point. The null hypothesis can be thought of as an easy place to put the start of your ruler.
If a null hypothesis is rejected, an alternate hypothesis must be adopted in its place. The null and alternate hypotheses must be mutually exclusive, but must also between them describe all situations. If a null hypothesis is ‘no difference exists’ then the alternate should be simply ‘a difference exists’.
The components of a hypothesis test can be readily described using the acronym GOST: identify the Groups you wish to compare; define the Outcome to be measured; collect and Summarise the data; then evaluate the likelihood of the null hypothesis, using a Test statistic .
When considering groups, think first about how many. Is there just one group being compared against an audit standard, or are you comparing one group with another? Some studies may wish to compare more than two groups. Another situation may involve a single group measured at different points in time, for example before or after a particular treatment. In this situation each participant is compared with themselves, and this is often referred to as a ‘paired’ or a ‘repeated measures’ design. It is possible to combine these types of groups—for example a researcher may measure arterial BP on a number of different occasions in five different groups of patients. Such studies can be difficult, both to analyse and interpret.
In other studies we may want to see how a continuous variable (such as age or height) affects the outcomes. These techniques involve regression analysis, and are beyond the scope of this article.
The outcome measures are the data being collected. This may be a continuous measure, such as temperature or BMI, or it may be a categorical measure, such as ASA status or surgical specialty. Often, inexperienced researchers will strive to collect lots of outcome measures in an attempt to find something that differs between the groups of interest; if this is done, a ‘primary outcome measure’ should be identified before the research begins. In addition, the results of any hypothesis tests will need to be corrected for multiple measures.
The summary and the test statistic will be defined by the type of data that have been collected. The test statistic is calculated then transformed into a p- value using tables or software. It is worth looking at two common tests in a little more detail: the χ 2 test, and the t -test.
The χ 2 test of independence is a test for comparing categorical outcomes in two or more groups. For example, a number of trials have compared surgical site infections in patients who have been given different concentrations of oxygen perioperatively. In the PROXI trial, 4 685 patients received oxygen 80%, and 701 patients received oxygen 30%. In the 80% group there were 131 infections, while in the 30% group there were 141 infections. In this study, the groups were oxygen 80% and oxygen 30%, and the outcome measure was the presence of a surgical site infection.
The summary is a table ( Table 1 ), and the hypothesis test compares this table (the ‘observed’ table) with the table that would be expected if the proportion of infections in each group was the same (the ‘expected’ table). The test statistic is χ 2 , from which a p- value is calculated. In this instance the p -value is 0.64, which means that results like this would occur 64% of the time if the null hypothesis were true. We thus have no evidence to reject the null hypothesis; the observed difference probably results from sampling variation rather than from an inherent difference between the two groups.
Summary of the results of the PROXI trial. Figures are numbers of patients.
Group | |||
---|---|---|---|
Oxygen 80% | Oxygen 30% | ||
Outcome | Infection | 131 | 141 |
No infection | 554 | 560 | |
Total | 685 | 701 |
The t- test is a statistical method for comparing means, and is one of the most widely used hypothesis tests. Imagine a study where we try to see if there is a difference in the onset time of a new neuromuscular blocking agent compared with suxamethonium. We could enlist 100 volunteers, give them a general anaesthetic, and randomise 50 of them to receive the new drug and 50 of them to receive suxamethonium. We then time how long it takes (in seconds) to have ideal intubation conditions, as measured by a quantitative nerve stimulator. Our data are therefore a list of times. In this case, the groups are ‘new drug’ and suxamethonium, and the outcome is time, measured in seconds. This can be summarised by using means; the hypothesis test will compare the means of the two groups, using a p- value calculated from a ‘ t statistic’. Hopefully it is becoming obvious at this point that the test statistic is usually identified by a letter, and this letter is often cited in the name of the test.
The t -test comes in a number of guises, depending on the comparison being made. A single sample can be compared with a standard (Is the BMI of school leavers in this town different from the national average?); two samples can be compared with each other, as in the example above; or the same study subjects can be measured at two different times. The latter case is referred to as a paired t- test, because each participant provides a pair of measurements—such as in a pre- or postintervention study.
A large number of methods for testing hypotheses exist; the commonest ones and their uses are described in Table 2 . In each case, the test can be described by detailing the groups being compared ( Table 2 , columns) the outcome measures (rows), the summary, and the test statistic. The decision to use a particular test or method should be made during the planning stages of a trial or experiment. At this stage, an estimate needs to be made of how many test subjects will be needed. Such calculations are described in detail elsewhere. 5
The principle types of hypothesis test. Tests comparing more than two samples can indicate that one group differs from the others, but will not identify which. Subsequent ‘post hoc’ testing is required if a difference is found.
Type of data | Number of groups | ||||
---|---|---|---|---|---|
1 (comparison with a standard) | 1 (before and after) | 2 | More than 2 | Measured over a continuous range | |
Categorical | Binomial test | McNemar's test | χ test, or Fisher's exact (2×2 tables), or comparison of proportions | χ test | Logistic regression |
Continuous (normal) | One-sample -test | Paired -test | Independent samples -test | Analysis of variance (ANOVA) | Regression analysis, correlation |
Continuous (non-parametric) | Sign test (for median) | Sign test, or Wilcoxon matched-pairs test | Mann–Whitney test | Kruskal–Wallis test | Spearman's rank correlation |
Although hypothesis tests have been the basis of modern science since the middle of the 20th century, they have been plagued by misconceptions from the outset; this has led to what has been described as a crisis in science in the last few years: some journals have gone so far as to ban p -value s outright. 6 This is not because of any flaw in the concept of a p -value, but because of a lack of understanding of what they mean.
Possibly the most pervasive misunderstanding is the belief that the p- value is the chance that the null hypothesis is true, or that the p- value represents the frequency with which you will be wrong if you reject the null hypothesis (i.e. claim to have found a difference). This interpretation has frequently made it into the literature, and is a very easy trap to fall into when discussing hypothesis tests. To avoid this, it is important to remember that the p- value is telling us something about our sample , not about the null hypothesis. Put in simple terms, we would like to know the probability that the null hypothesis is true, given our data. The p- value tells us the probability of getting these data if the null hypothesis were true, which is not the same thing. This fallacy is referred to as ‘flipping the conditional’; the probability of an outcome under certain conditions is not the same as the probability of those conditions given that the outcome has happened.
A useful example is to imagine a magic trick in which you select a card from a normal deck of 52 cards, and the performer reveals your chosen card in a surprising manner. If the performer were relying purely on chance, this would only happen on average once in every 52 attempts. On the basis of this, we conclude that it is unlikely that the magician is simply relying on chance. Although simple, we have just performed an entire hypothesis test. We have declared a null hypothesis (the performer was relying on chance); we have even calculated a p -value (1 in 52, ≈0.02); and on the basis of this low p- value we have rejected our null hypothesis. We would, however, be wrong to suggest that there is a probability of 0.02 that the performer is relying on chance—that is not what our figure of 0.02 is telling us.
To explore this further we can create two populations, and watch what happens when we use simulation to take repeated samples to compare these populations. Computers allow us to do this repeatedly, and to see what p- value s are generated (see Supplementary online material). 7 Fig 1 illustrates the results of 100,000 simulated t -tests, generated in two set of circumstances. In Fig 1 a , we have a situation in which there is a difference between the two populations. The p- value s cluster below the 0.05 cut-off, although there is a small proportion with p >0.05. Interestingly, the proportion of comparisons where p <0.05 is 0.8 or 80%, which is the power of the study (the sample size was specifically calculated to give a power of 80%).
The p- value s generated when 100,000 t -tests are used to compare two samples taken from defined populations. ( a ) The populations have a difference and the p- value s are mostly significant. ( b ) The samples were taken from the same population (i.e. the null hypothesis is true) and the p- value s are distributed uniformly.
Figure 1 b depicts the situation where repeated samples are taken from the same parent population (i.e. the null hypothesis is true). Somewhat surprisingly, all p- value s occur with equal frequency, with p <0.05 occurring exactly 5% of the time. Thus, when the null hypothesis is true, a type I error will occur with a frequency equal to the alpha significance cut-off.
Figure 1 highlights the underlying problem: when presented with a p -value <0.05, is it possible with no further information, to determine whether you are looking at something from Fig 1 a or Fig 1 b ?
Finally, it cannot be stressed enough that although hypothesis testing identifies whether or not a difference is likely, it is up to us as clinicians to decide whether or not a statistically significant difference is also significant clinically.
As mentioned above, some have suggested moving away from p -values, but it is not entirely clear what we should use instead. Some sources have advocated focussing more on effect size; however, without a measure of significance we have merely returned to our original problem: how do we know that our difference is not just a result of sampling variation?
One solution is to use Bayesian statistics. Up until very recently, these techniques have been considered both too difficult and not sufficiently rigorous. However, recent advances in computing have led to the development of Bayesian equivalents of a number of standard hypothesis tests. 8 These generate a ‘Bayes Factor’ (BF), which tells us how more (or less) likely the alternative hypothesis is after our experiment. A BF of 1.0 indicates that the likelihood of the alternate hypothesis has not changed. A BF of 10 indicates that the alternate hypothesis is 10 times more likely than we originally thought. A number of classifications for BF exist; greater than 10 can be considered ‘strong evidence’, while BF greater than 100 can be classed as ‘decisive’.
Figures such as the BF can be quoted in conjunction with the traditional p- value, but it remains to be seen whether they will become mainstream.
The author declares that they have no conflict of interest.
The associated MCQs (to support CME/CPD activity) will be accessible at www.bjaed.org/cme/home by subscribers to BJA Education .
Jason Walker FRCA FRSS BSc (Hons) Math Stat is a consultant anaesthetist at Ysbyty Gwynedd Hospital, Bangor, Wales, and an honorary senior lecturer at Bangor University. He is vice chair of his local research ethics committee, and an examiner for the Primary FRCA.
Matrix codes: 1A03, 2A04, 3J03
Supplementary data to this article can be found online at https://doi.org/10.1016/j.bjae.2019.03.006 .
The following is the Supplementary data to this article:
(12 reviews)
Andrea M. Nelson, University of West Florida
Katherine Greene, University of West Florida
Copyright Year: 2021
Publisher: University of West Florida Pressbooks
Language: English
Conditions of use.
Learn more about reviews.
Reviewed by Carolina Molina-Martin, Adjunct Faculty, Old Dominion University on 6/16/24
This book is comprehensive and informative. In addition to a Table of Contents that provides a breakdown of each of the 18 chapters, a Glossary follows the Table of Contents. Glossary terms are bolded in green and their definitions can be found in... read more
Comprehensiveness rating: 5 see less
This book is comprehensive and informative. In addition to a Table of Contents that provides a breakdown of each of the 18 chapters, a Glossary follows the Table of Contents. Glossary terms are bolded in green and their definitions can be found in the glossary at the end of the book.
Content Accuracy rating: 5
The content is very accurate, . It is non biased, and inclusive. The chapters are very thorough and well-written. There are no glaring errors.
Relevance/Longevity rating: 5
The information presented in the text is relevant and is not information that will become outdated, as it is focused on medical terminology.
Clarity rating: 5
The book is written in very accessible language. Terms are presented with appropriate depth and clarity. Each chapter opens with a list of word parts (prefixes, combining forms, and suffixes) related to the topic. References are included with each chapter.
Consistency rating: 5
The terminology and framework are consistent, Interactive content is built into each chapter. The structure of each chapter is consistent throughout. It starts with learning objectives.
Modularity rating: 5
The book is easy to read. It is written with well defined chapters broken into manageable paragraphs.
Organization/Structure/Flow rating: 5
Extremely well organized. The order of presentation is overall logical and clear. Pertinent information for the topic of the chapter is covered.
Interface rating: 5
The interactive reinforcement activities require you to click, drag and drop, listen and repeat, flip, and test yourself. No issues were found with the features of the text. The interface is user-friendly, No problems with navigation.
Grammatical Errors rating: 5
No glaring errors.
Cultural Relevance rating: 5
The text is not culturally insensitive.
This OER book is different from many traditional medical terminology textbooks. Kudos to the authors for all of their hard work on creating such a wonderful book. This resource will serve well future healthcare students and any healthcare profession.
Reviewed by Judith Guetzow, Lecturer II, University of Texas Rio Grande Valley on 5/22/24
The text offers comprehensive coverage of medical terminology for healthcare professions, presenting terms with appropriate depth and clarity. Each chapter opens with a list of word parts (prefixes, combining forms, and suffixes) related to the... read more
The text offers comprehensive coverage of medical terminology for healthcare professions, presenting terms with appropriate depth and clarity. Each chapter opens with a list of word parts (prefixes, combining forms, and suffixes) related to the topic. Medical terms are prominently displayed in bold green font throughout the chapters, and a useful glossary is provided in the book, aiding students in quickly locating relevant vocabulary. Furthermore, at the end of each body system chapter, a vocabulary list is included, featuring terms associated with that specific system.
The content is accurate, error-free, unbiased, and reflects the latest developments in the field; thus, providing students with reliable information essential for their understanding of medical terminology.
The content is current, ensuring students learn terminology that reflects existing practices. The authors provide a balance between current information and the established principles of medical terminology, ensuring the text remains relevant without quickly becoming obsolete. Its structured format allows for easy updates as medical terminology evolves.
Written in clear and accessible prose, the text provides explanations and context for technical terms, making it suitable for students at all proficiency levels and enhancing overall clarity.
Consistency is maintained throughout the text in both terminology and framework; thus, contributing to its reliability and lucidity. This is conducive to improving students’ comprehension and retention of the medical terms.
The modularity of the text facilitates flexible teaching approaches. The text is organized into eighteen chapters that can be further divided into reduced sections, allowing instructors to assign smaller reading sections without disrupting the flow of the material.
The topics in the text are presented in a logical, clear fashion guiding students through progressively more complex concepts in a clear and discerning manner. As students advance through each body system, they learn the corresponding medical terms, the word parts that form these terms, and the relevant abbreviations specific to each system.
The interface of the textbook is user-friendly, free of significant navigation issues or display problems that could hinder the learning experience. There is a clear presentation of images and charts which enhances the learning experience. Each chapter integrates interactive content such as videos, flashcards, drag-and-drop exercises, and self-tests that are exclusive to the online format. Hyperlinks to the interactive content are provided to users of PDF or EPUB versions of the text. This content was accessible to a diverse range of learners, with closed captioning provided for the videos, and no errors were detected in the captions. Additionally, image descriptions were included for each picture. A minor concern is that certain flashcards lacked the text-to-speech feature.
No grammatical errors were found. The text is grammatically sound and written at a level appropriate for the students, ensuring readability.
The material is free from inappropriate or offensive content.
Overall, the authors created a comprehensive textbook that provides a thorough understanding of medical terminology relating to body systems and pathology, diagnostics, and medical procedures. It would be wonderful if PowerPoints, test banks, and assignments as learning exercises that require students to break down the terms into word parts were included in each chapter. However, I found reading the text and engaging with the interactive activities enjoyable. Reviewing this material has been valuable, as it has piqued my interest in potentially using it for my medical terminology class in the near future.
Reviewed by Gary McIlvain, Professor, Marshall University on 5/21/24
It covers the information very well. It tends to become an anatomy textbook too much. read more
It covers the information very well. It tends to become an anatomy textbook too much.
The accuracy was on par. Again, too much "anatomy book" context for med term.
With anatomy and medical terminology, it rarely changes. So, the text longevity would be good.
The ease of reading the text is great and students would be able to follow it well. It seems to become a better anatomy text than medical terminology or maybe the title should be "applied medical terminology" and it state it focuses on applying it within anatomy.
Yes, but more anatomy textbook than I would use for a med term class.
It is divided by systems, which is a common way to organize a medical terminology text.
It does do a good job applying it to common every day issues (e.g. M.S.)
Great use of drag and drop and flash cards.
I didn't note any errors.
Yes, it did use pictures that depicted varying racx3es, ethnicities, and backgrounds. It is limiting in anatomy pictures to be able to do this...
I would like to see basic pharmacology used in it. I would not currently use this as the only text due to the lack of basic pharmacology. With that added it would be a great text.
Reviewed by Jenni Johnson, Assistant Professor, Marshall University on 5/21/24
This book provides appropriate medical terminology for all regions of the body as well as all healthcare disciplines. It is a great asset for any healthcare profession. read more
This book provides appropriate medical terminology for all regions of the body as well as all healthcare disciplines. It is a great asset for any healthcare profession.
I found no mistakes within the textbook.
The book is extremely relevant and it can be utilized for many years across many healthcare professions. This text is also good for a variety of learning styles by utilizing virtual flash cards and videos with audio.
The textbook is clearly and concisely written
Each chapter follows the same format which makes it very easy to navigate.
This book has 18 chapters and they are clearly outlined. Each chapter is broken up into sections that have an excellent flow that builds learning over time.
Each area is clearly defined
Each chapter and learning tool is easy to navigate and there were no technical issues.
I found no grammatical errors in the text.
There was no cultural bias in this text. It was inclusive of all cultures, and genders and free from religious bias.
I believe this text can be used for a wide variety of future healthcare professions. The flashcards, interactive videos and end of chapter quizzes appeal to all learning styles and assist with retention. Each word is broken down to easily understand the meaning and use of the terminology,
Reviewed by Wendy Schuh, Assistant Professor, Minnesota State University Mankato on 2/8/24
This book is clearly laid out with 18 different chapters covering all of the body systems + obstetrics. There are interactive figures, flash cards, and end of chapter quizzes. Vocabulary words have a linked definition within the text. It would be... read more
This book is clearly laid out with 18 different chapters covering all of the body systems + obstetrics. There are interactive figures, flash cards, and end of chapter quizzes. Vocabulary words have a linked definition within the text. It would be an added benefit to include pronunciation, which is an important component of medical terminology. Videos have a captioning option.
No concerns with accuracy.
References are included with each chapter. Publication date is 2021, and most references are within the last five years. In addition, this content is mostly stable over the years. CrashCourse videos are a little older (2015) but many students are familiar with Hank Green in this format. Information is relevant and easy to process.
Clear chapter content, sections, and headings.
Consistent style of writing, activities, page layout, etc. throughout the book.
Chapters organized in a logical manner. Flashcards and interactive body part activities are wonderful tools, even better since they can be completed multiple times.
Organization/Structure/Flow rating: 4
The structure of the textbook is sound and consistent with other medical terminology textbooks. A more thorough Table of Contents would allow for easier navigation. It has a good balance of technical and non-technical writing that makes it easy to read and comprehend.
Interface rating: 3
Appealing and interactive. I attempted to take advantage of the “re-use” option below each activity but could not figure it out. Search function does not work well. I tried searching phrases directly from the text, and it would not pull up. The labeling activities were difficult to complete as the drag and drop feature would not scroll. Therefore, it would be useful to have a correct answer option to see the completed figure. It would be helpful to have descriptions included with different e-book options that explain interactive functions with each format.
Very clean and proofed!
Appeared to be culturally inclusive, although it is difficult to assess in this type of resource. No diverse representation of skin color on diagrams.
This is a great textbook that mimics other medical terminology textbooks costing $100+ that don’t have interactive components. There could be some great additions to more effectively use this for a course textbook, such as a question bank, study guides, and suggestions for worksheets and projects to incorporate points into a course framework.
Reviewed by Sharon Schaeffer, Associate Clinical Professor, Bowling Green State University on 4/16/23
Covers major body systems . read more
Covers major body systems .
I did not see any errors during my review.
Medical terminology is a pretty static topic. When students learn how to correctly combine forms, they will be ready to decipher new vocabulary that comes with progress in health care.
Easy to understand.
The depth of content is consistent.
I will allow students to choose their topic of the week after the first 3 chapters are complete. The module system will work well for this design. This design allows students taking A & P or similar courses the opportunity to learn med term at the same time as they are learning in other courses.
Well organized.
I had no challenges linking to and using the added features.
No problems noted.
Inclusive content.
This book will help my students learn the basics of medical terminology as a foundation for building a strong professional vocabulary. I like the interactive activities in this book as it helps learners of different styles. It would be a bonus if there were quiz question banks available. It is not enough of a deal breaker to stop me from using this in my course next Spring semester.
Reviewed by Kristin Meyer, Professor, Drake University on 12/15/22
The text comprehensively covers medical terms in each body system, with a couple of introductory chapters. It covers the span of life with a dedicated obstetrics chapter, which I have not seen in other texts. read more
The text comprehensively covers medical terms in each body system, with a couple of introductory chapters. It covers the span of life with a dedicated obstetrics chapter, which I have not seen in other texts.
No inaccuracies identified.
Medical terminology does not easily or often change, but the text could be easily updated from time to time to include new disease states or terms.
No issues with clarity identified.
Each chapter has a consistent format with link to video overview and active learning activities interspersed throughout.
The organization by body system allows an instructor to assign the appropriate amount of content to correspond with course credit hours.
The online version is easy to navigate. The search function doesn't work as I would expect it to.
Interface rating: 4
The online version is easy to navigate. The pdf download has none of the interactive features. It would be nice if the pdf version could somehow include the active learning exercises in each chapter, with an answer key appendix.
No grammatical errors identified.
Does not appear to be culturally insensitive.
I could easily adopt this text for my web-instructed undergraduate medical terminology class. The interactive features are helpful to engage students. A summary quiz at the end of each chapter would be a nice added feature.
Reviewed by Nancy Bouchard, Adjunct Professor, North Shore Community College on 11/14/22
Very well done. read more
Very well done.
Very accurate and not biased.
If updates are needed, they could be added with ease.
Well written text.
Very consistent.
Very user friendly. Easy to read and assign chapters.
Very organized.
I did not encounter any issues.
None noticed.
Not insensitive or offensive.
My only concern is for the student who has no prior exposure to medical terminology, healthcare training or will not have a clinical role in healthcare. I would not want them to get overwhelmed by the depth of detail in each chapter. I would suggest a section in each chapter that contains exercises for students to test their understanding of the subject matter read, practice correctly writing the terms and the like. Visual learning is only one way for students to absorb content. I would have to create ways to test their understanding to be graded using quizzes, a research project, midterm and final exam. I'm on the fence if the content in the textbook is too deep for only needing a basic understanding of medical terms.
Reviewed by Martha Fabian-Krause, Adjunct Clinical Instructor, Rogue Community College on 9/1/22
Systematic flow of each body system to include root word, prefix, suffix, anatomy, physiology, video and practice in each section. Logical to follow. read more
Systematic flow of each body system to include root word, prefix, suffix, anatomy, physiology, video and practice in each section. Logical to follow.
No issues noted. Very accurate.
Timeless interpretation of terminology would make the on line text need updating only if new medical information becomes available.
Detailed explanations of terminology, anatomy and physiology with pertinent examples and word practice at the end of each body system.
Each section is consistent by acknowledging medical diseases, disorders, and procedures related to the root words. Good follow through in each body system.
This on line book can be assigned in a particular order relevant to other class material and does not need to be completed in any particular time frame. Pleasurable reading.
The format of each section (body system) is in a progressive fashion and is put together with a video near the beginning and word games at the end of each section. Good sequencing noted throughout.
Charts are easy to navigate. There is an identical format what is easy to assimilate.
None noted.
No diversive issues noted. Represents the full spectrum of human anatomy and physiology.
Marvelous understanding of the root words, prefix, suffix and detailed anatomy and physiology. The videos and word matches at the end of each section put the meaning crystal clear.
Reviewed by Carla Tobin, Faculty, Century College on 6/17/22
This textbook covers all of the body systems, the word parts and rules, and prefixes and suffixes. read more
This textbook covers all of the body systems, the word parts and rules, and prefixes and suffixes.
This book is very accurate. No discrepancies or errors were noted in the textbook.
Medical terminology is a subject that does not change over the years. As new diseases and technologies arise, they can easily be incorporated into the content.
The language used in the book is clear and pronunciations of the terminology is provided throughout the e-book. This is an easy to read book for high school or college level students.
The chapters are consistent in there format and organization throughout the textbook. It is easy to follow for the student.
The chapters are broken down into sections which make it easy to read. The videos are shown within the textbook, so the user is not taken to another site. One suggestion would be to have a link to the next chapter at the bottom of the page rather than scrolling up to the top to choose the next chapter from the left side menu.
The organization of this textbook is exactly what you would expect for a Medical Terminology textbook. It is divided into chapters by body system.
There are no apparent issues with the interface. As noted above, the videos are shown within the textbook window, so the user is not taken to another site.
I did not note any grammatical errors in this textbook.
Cultural sensitivity is not really relevant with medical terminology. This language is used in many countries in order to be able to communicate in the same language.
I agree that the best use of this book in the online internet version. This is a very comprehensive medical terminology book. It covers all of the body systems and word building of medical terminology. The chapters provide many opportunities to practice what the student has learned. I liked that each chapter has the learning objectives listed at the beginning. I would have liked to see chapter summaries for the students to study. I think that this book could easily be incorporated into an online class, however, some work would be involved making PowerPoints, homework and quizzes. Overall, this is an excellent Medical Terminology book.
Reviewed by Renee Eaton, Advanced Instructor, Undergraduate Director, Virginia Tech on 5/17/22
Systems-based organization and includes all body systems. read more
Systems-based organization and includes all body systems.
No errors or issues noted
Medical terminology is something that rarely changes. Context activities may change over time, as does disease prevalence and knowledge, but new terms or different terms are not common.
Clear descriptions and use of technical and non-technical language.
The organization is the same across each chapter making the book easy to access and navigate. Language and flow are consistent.
Text is easy to navigate. It may be helpful to provide some in-chapter navigation on the lower menu bar. For example, the previous and next chapters are linked on the left and right margins of the bottom, and chapter components such as diseases / anatomy / etc. could be added to the center. It may not all fit, but even having a couple of navigation points within the chapter would be helpful.
Good organization and order of chapters.
This is one of my greatest difficulties. Navigation within chapters would be helpful. The incorporation of activities, particularly the labeling activities and Medical Terms in Context, are difficult with a regular laptop screen. The text and answer selections are often not on the same screen, making the activity more tedious to complete. Some of the labeling activities also have large images that put the image and answer selections on different screens. The "Did You Know", "Objectives" and colored boxes contain wasted space. They're excessively large especially in the header, and when viewing on a laptop is often half the screen if not more. The PDF version often has issues of inconsistent font size and misalignment of tables.
No grammatical errors found. I appreciated the bold and linked words, with the ability to see definitions with one click. It might be helpful to have a sidebar with the important words and definitions / information in the section, but that might not be feasible with formatting.
Hard to assess for medical terminology.
The best way to use this text is online with solid internet. The PDF version is frustrating as there are no activities or practice opportunities, and there are issues with the organization and appearance such as misaligned tables and font size differences. When internet is good but not great, none of the videos are viewable. The activities and practice opportunities in the online book are very helpful and enjoyable. Their length is appropriate to encourage use and they are strategically placed throughout the chapters. I did have trouble with the search tool, as things I entered went to the glossary but always included the beginning of the glossary. For example, a search for "diplopia" showed the following:
Abdominal Pertaining to the abdomen (National Cancer Institute, n.d.) Abdominoplasty Surgical repair of the abdomen (National Library of Medicine, 2021) Abduction Moving the limb or hand laterally away from the body, or spreading the fingers or toes (Betts et al., 2013) Abductor Moves the bone away from the midline (Betts et al., 2013) Ablation The Read more » Sensory Systems
Learning Objectives Examine the anatomy of the sensory systems Determine the main functions of the sensory systems Differentiate the medical terms of the sensory systems and common abbreviations Discover the medical specialties associated with the sensory systems Recognize common diseases, disorders, and procedures related to the sensory systems Sensory Systems Word Parts Click on prefixes, Read more »
Overall, the authors did a wonderful job of developing a thorough and practical text. I appreciate the thought that went into the interactive nature of the book and the availability to exercises to practice knowledge.
Reviewed by Debra Minzola, Associate Professor, Bloomsburg University of Pennsylvania on 3/18/22
This textbook is very inclusive in the content area. It not only discusses the word but breaks down medical terminology to help learners to easily decipher the meaning of a medical term . read more
This textbook is very inclusive in the content area. It not only discusses the word but breaks down medical terminology to help learners to easily decipher the meaning of a medical term .
There was no inaccuracies detected throughout the text.
This text is very relevant and will easily be updated if needed.
This is an easy to read text and would be a valuable resource for new learners. The ebook offers videos and learning activities throughout.
The text is internally consistent with an easy to follow framework.
The modules in this text are easy to navigate and locate specialty sections.
This text is clearly organized and easy to navigate.
There is no significant navigation problems or confusing features.
There is clear grammar throughout the text.
There is no offensive content in this textbook or language that can be viewed as culturally insensitive.
Learning objectives are listed at the introduction of each section followed by a guide on how to break down each system's medical terms. Throughout each section there are diagrams, charts, and additional videos in the ebook which reinforces the content. The book is organized and easy to navigate.
About the book.
Medical Terminology for Healthcare Professions is an Open Educational Resource (OER) that focuses on breaking down, pronouncing, and learning the meaning of medical terms within the context of anatomy and physiology. This resource is targeted for Healthcare Administration, Health Sciences, and Pre-Professional students.
Andrea M. Nelson , PT, DPT, GCS, CLT, University of West Florida
Katherine Greene , MPH, University of West Florida
Use this collection of resources to find the style of learning medical terminology that suits you best. Have a suggestion or a favorite resource you would like us to include? Contact [email protected] to let us know.
Provided by the National Library of Medicine
The definition of medical terminology.
Medical terminology is the specialized language used in healthcare to accurately describe the human body, its parts, functions, and the procedures performed on it.
Identifiers:
Medical terminology often sounds complex, but once you understand the basic structure of medical words, it becomes much easier to parse their meanings. Most medical terms have three key elements: roots, prefixes, and suffixes. Each part of the word provides a clue to its definition, related body systems, and functions.
By dissecting a medical term into these components, you can begin to understand even the most intimidating terminology. Familiarizing yourself with common roots, prefixes, and suffixes can significantly enhance your ability to understand medical discussions, documents, and information more broadly.
When and where is Medical Terminology used?
The information provided here is for educational and entertainment purposes only. It is not intended as, nor should it be considered a substitute for professional medical advice, diagnosis, or treatment. Always seek the advice of your physician or other qualified health provider with any questions you may have regarding a medical condition. If you think you may have a medical emergency, immediately call 911 or your local emergency number.
Search medical terms and abbreviations with the most up-to-date and comprehensive medical dictionary from the reference experts at Merriam-Webster. Master today's medical vocabulary. Become an informed health-care consumer!
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BMC Medical Education volume 24 , Article number: 947 ( 2024 ) Cite this article
Metrics details
Nearly three in four U.S. medical students graduate with debt in six-figure dollar amounts which impairs students emotionally and academically and impacts their career choices and lives long after graduation. Schools have yet to develop systems-level solutions to address the impact of debt on students’ well-being. The objectives of this study were to identify students at highest risk for debt-related stress, define the impact on medical students’ well-being, and to identify opportunities for intervention.
This was a mixed methods, cross-sectional study that used quantitative survey analysis and human-centered design (HCD). We performed a secondary analysis on a national multi-institutional survey on medical student wellbeing, including univariate and multivariate logistic regression, a comparison of logistic regression models with interaction terms, and analysis of free text responses. We also conducted semi-structured interviews with a sample of medical student respondents and non-student stakeholders to develop insights and design opportunities.
Independent risk factors for high debt-related stress included pre-clinical year (OR 1.75), underrepresented minority (OR 1.40), debt $20–100 K (OR 4.85), debt >$100K (OR 13.22), private school (OR 1.45), West Coast region (OR 1.57), and consideration of a leave of absence for wellbeing (OR 1.48). Mental health resource utilization ( p = 0.968) and counselors ( p = 0.640) were not protective factors against debt-related stress. HCD analysis produced 6 key insights providing additional context to the quantitative findings, and associated opportunities for intervention.
We used an innovative combination of quantitative survey analysis and in-depth HCD exploration to develop a multi-dimensional understanding of debt-related stress among medical students. This approach allowed us to identify significant risk factors impacting medical students experiencing debt-related stress, while providing context through stakeholder voices to identify opportunities for system-level solutions.
Peer Review reports
Over the past few decades, it has become increasingly costly for aspiring physicians to attend medical school and pursue a career in medicine. Most recent data shows that 73% of medical students graduate with debt often amounting to six-Fig [ 1 ]. – an amount that is steadily increasing every year [ 2 ]. In 2020, the median cost of a four-year medical education in the United States (U.S.) was $250,222 for public and $330,180 for private school students [ 1 ] – a price that excludes collateral costs such as living, food, and lifestyle expenses. To meet these varied costs, students typically rely on financial support from their families, personal means, scholarships, or loans. Students are thereby graduating with more debt than ever before and staying indebted for longer, taking 10 to 20 years to repay their student loans regardless of specialty choice or residency length [ 1 ].
Unsurprisingly, higher debt burden has been negatively correlated with generalized severe distress among medical students [ 3 , 4 ], in turn jeopardizing their academic performance and potentially impacting their career choices [ 5 ]. Studies have found that medical students with higher debt relative to their peers were more likely to choose a specialty with a higher average annual income [ 5 ], less likely to plan to practice in underserved locations, and less likely to choose primary care specialties [ 4 ]. However, a survey of 2019 graduating medical students from 142 medical schools found that, when asked to rank factors that influenced their specialty choice, students ranked economic factors, including debt and income, at the bottom of the list. With this inconsistency in the literature, authors Youngclaus and Fresne declare that further studies and analysis are required to better understand this important relationship [ 1 ].
Unfortunately, debt and its negative effects disproportionately impact underrepresented minority (URM) students, including African Americans, Hispanic Americans, American Indian, Native Hawaiian, and Alaska Native [ 6 ], who generally have more debt than students who are White or Asian American [ 1 ]. In 2019, among medical school graduates who identified as Black, 91% reported having education debt, in comparison to the 73% reported by all graduates [ 1 ]. Additionally, Black medical school graduates experience a higher median education debt amount relative to other groups of students, with a median debt of $230,000 [ 1 ]. This inequitable distribution of debt disproportionately places financial-related stress on URM students [ 7 ], discouraging students from pursuing a medical education [ 8 ]. These deterring factors can lead to a physician workforce that lacks diversity and compromises health equity outcomes [ 9 ].
Limited literature exists to identify the impact of moderating variables on the relationship between debt and debt-related stress. Financial knowledge is found to be a strong predictor of self-efficacy and confidence in students’ financial management, leading to financial optimism and potentially alleviating debt stress [ 10 , 11 , 12 ]. Numerous studies list mindfulness practices, exercise, and connecting with loved ones as activities that promote well-being and reduce generalized stress among students [ 13 , 14 , 15 ]. However, to date, no studies have examined whether these types of stress-reducing activities, by alleviating generalized stress, reduce debt-related stress. Studies have not examined whether resources such as physician role models may act as a protective factor against debt-related stress.
Despite the growing recognition that debt burdens medical students emotionally and academically, we have yet to develop systemic solutions that target students’ unmet needs in this space. We performed the first multi-institutional national study on generalized stress among medical students, and found that debt burden was one of several risk factors for generalized stress among medical students [ 3 ]. The goal of this study is to build upon our findings by using a mixed methods approach combining rigorous survey analysis and human-centered design to develop an in-depth understanding of the impact that education debt has on medical students’ emotional and academic well-being and to identify opportunities for intervention.
We conducted a mixed methods, cross-sectional study that explored the impact of debt-related stress on US medical students’ well-being and professional development. This study was conducted at the University of California, San Francisco (UCSF). All activities were approved by the UCSF institutional review board, and informed consent was obtained verbally from participants prior to interviews. We performed a secondary analysis of the quantitative and qualitative results of the Medical Student Wellbeing Survey (MSWS), a national multi-institutional survey on medical student wellbeing administered between 2019 and 2020, to determine risk factors and moderating variables of debt-related stress. To further explore these variables, we used human-centered design (HCD), an approach to problem-solving that places users at the center of the research process in order to determine key pain points and unmet needs, and co-design solutions tailored to their unique context [ 16 ]. In this study, we performed in-depth, semi-structured interviews with a purposefully sampled cohort of medical students and a convenience sample of non-student stakeholders to determine key insights representing students’ unmet needs, and identified opportunities to ameliorate the impact of debt-related stress on medical students.
The MSWS is a survey to assess medical student wellbeing that was administered from September 2019 to February 2020 to medical students actively enrolled in accredited US or Caribbean medical schools [ 3 ]. Respondents of the MSWS represent a national cohort of > 3,000 medical students from > 100 unique medical school programs. The MSWS utilizes a combination of validated survey questions, such as the Medical Student Wellbeing Index (MS-WBI), and questions based on foundations established from previously validated wellbeing survey methods [ 3 ]. Questions generally focused on student demographics, sources of stress during medical school, specialty consideration, and frequency in activities that promote wellbeing. Some questions ask students to rate physical, emotional, and social domains of wellbeing using a five-point Likert scale. Questions of interest from the MSWS included debt-related stress, generalized stress, intended specialty choice, and utilization of well-being resources and counselors. An additional variable investigated was average school tuition, which was determined by a review of publicly available data for each student’s listed medical school [ 17 ]. All data from the MSWS was de-identified for research purposes.
Debt stress was assessed by the question, “How does financial debt affect your stress level?” Students responded using a five-point Likert scale from − 2 to 2: significant increase in stress (-2), mild increase (-1), no change (0), mild decrease (1), or significant decrease (2). Responses for this question were evaluated as a binary index of ‘high debt stress,’ defined as a response of − 2, versus ‘low debt stress,’ defined as a response of − 1 or 0. In addition, generalized stress from the MSWS was assessed by questions from the embedded MS-WBI, which produced a score. Previous studies have shown that the score can be used to create a binary index of distress: a score ≥ 4 has been associated with severe distress, and a score < 4 has been associated with no severe distress [ 18 ].
We categorized students’ responses to intended specialty choice by competitiveness, using the 2018 National Resident Match Program data [ 19 ]. ‘High’ and ‘low’ competitiveness were defined as an average United States Medical Licensing Examination (USMLE) Step 1 score of > 240 or ≤ 230, respectively, or if > 18% or < 4% of applicants were unmatched, respectively. ‘Moderate’ competition was defined as any specialty not meeting criteria for either ‘high’ or ‘low’ competitiveness.
The MSWS assessed the utilization of well-being resources by the question, “At your institution, which of the following well-being resources have you utilized? (Select all that apply)” Students responded by selecting each of the resource(s) they used: Mental Health and Counseling Services, Peer Mentorship, Self-Care Education, Mindfulness/Meditation Classes, Community Building Events, and Other. The number of choices that the student selected was calculated, allowing for placement into a category depending on the amount of resource utilization: 0–20%, 20–40%, 40–60%, 60–80%, 80–100%. Responses for this question were evaluated as a binary index of ‘high resource utilization,’ defined as a response of 80–100% resource utilization, versus ‘low resource utilization,’ defined as a response of < 80% resource utilization. The co-authors collaboratively decided upon this “top-box score approach,” [ 20 ] which is the sum of percentages for the most favorable top one, two or three highest categories on a scale, to assess if the most extreme users (80–100%) of these supportive resources experienced a decrease in debt-related stress. Additionally, use of a counselor for mental health support was assessed by the question, “Which of the following activities do you use to cope with difficult situations (or a difficult day on clinical rotation)? (Select all that apply).” Students responded by selecting the activities that they use from a list (e.g., listen to music, mindfulness practice, meet with a counselor, exercise). Responses for this question were evaluated as a binary index of ‘Meeting with a Counselor,’ defined by selection of that option, versus ‘Not Meeting with a Counselor,’ defined as not selecting that option.
We performed a secondary analysis of quantitative data from the MSWS to calculate frequencies and odds ratios for the five quantitative variables described above (debt-related stress, generalized stress, intended specialty, resource utilization, and school tuition). Tests performed are summarized in Table 1 (“Secondary Analysis Tests Performed”). Univariate analysis and multivariate logistic regression were performed among students in the high debt stress (-2) and low debt stress (0 or − 1) for select variables, such as clinical phase, URM, debt burden, specialty competitiveness, and average school tuition, to identify risk factors for high debt stress. To determine if ‘high resource utilization’ or ‘meeting with a counselor’ were moderating variables on the relationship between debt burden and debt stress, we applied the logistic regression with the interaction terms of ‘debt’ and ‘resource utilization’ (high vs. low). Then, we performed a similar analysis but replaced the interaction term with ‘debt’ and ‘meeting with a counselor’ (yes vs. no). We also performed Chi-squared tests to determine the degree to which severe distress increases as debt burden increases, if specialty competitiveness varied by debt stress, and if the proportion of students who identified as URM, in comparison to non-URM, differed by debt level. All statistical tests were two-sided and p < 0.05 was considered significant. Statistical analyses were performed using SAS version 9.4 and R version 4.0.5.
Free-text entries.
At the conclusion of the 2019–2020 MSWS, respondents had unlimited text space to provide comments to two prompts. The first prompt read, “What well-being resource(s), if offered at your school, do you feel would be most useful?” The second prompt read “If you have any further comments to share, please write them below.” Answers to either prompt that pertained to debt, cost of medical school, or finances were extracted for the purpose of this study and analyzed with the other qualitative data subsequently described.
Interview participants were identified from a repository of respondents to the MSWS who had attached their email address and expressed willingness at the time of the survey to be contacted for an interview [ 3 ]. Our recruitment period was between April 19, 2021 to July 2, 2021. The recruitment process involved sending invitations to all of the email addresses in the list to participate in a 45-minute interview on the topic of student debt and wellbeing. The invitation included a brief screening questionnaire asking students to report updates to questions that were previously asked in the MSWS (i.e.: clinical training year, marital status, dependents). Additional novel questions included primary financial support system, estimate of financial support systems’ household income in the last year, estimate of educational financial debt at conclusion of medical school, student’s plan for paying off debt, and degree of stress (using a Likert scale from 0 to 10) over current and future education debt.
Purposeful sampling of medical student stakeholders for interviews allowed us to maximize heterogeneity. We utilized the students’ responses to the brief screening questionnaire with their corresponding responses to demographic questions from the MSWS to select interviewees that varied by gender, race, presence of severe distress, type of medical school (public vs. private), region of school, and tuition level of school. The sampling ensured a diverse representation, in accordance with HCD methodology [ 21 ]. Brief descriptions of participant experiences are listed in Table 2 (“Interviewee Descriptors”). Students who were selected for interviews were sent a confirmation email to participate. Interviews were to be conducted until thematic saturation was reached. In addition, to include representation from the entire ecosystem, we interviewed a financial aid counselor at a medical school and a pre-medical student, chosen through convenience sampling. We directly contacted those two individuals for interviews.
All interviews were conducted between April 2021 and July 2021 over Zoom. A single researcher conducted interviews over an average of 45 min. Informed consent was obtained verbally from participants prior to interviews; interviews and their recordings only proceeded following verbal consent. The interview guide (S1 File) included open-ended questions about students’ experience of debt-related stress and their reflections on its consequences. The audio recordings were transcribed using Otter.ai, a secure online transcription service that converts audio files to searchable text files. Interview responses were redacted to preserve anonymity of respondent identity.
Interview data was analyzed using a general inductive approach to thematic analysis. Specifically, two researchers (SL and AY) independently inductively analyzed transcripts from the first three semi-structured interviews to come up with themes relating to the experiences and consequences of debt-related stress. They reconciled discrepancies in themes through discussion to create the codebook (S2 File), which included 18 themes. SL and AY independently coded each subsequent interview transcript as well as the free text responses from the survey, meeting to reach a consensus on representative quotes for applicable themes.
Following the HCD methodology, two researchers met with the core team to discuss the themes from the interviews and translate them into “insight statements”, which reflect key tensions and challenges experienced by stakeholders. Insight statements carefully articulate stakeholders’ unique perspectives and motivations in a way that is actionable for solution development [ 22 ]. As such, these insight statements are reframed into design opportunities, which suggest that multiple solutions are possible [ 23 , 24 ]. For example, discussion about themes 1a and 1b (“Questionable Job Security” and “Disappointing MD salary and Satisfaction Payoff”) revealed that they were related in the way that they led students to wonder whether the investment in medical school would be offset by the salary payoff. This led to the identification of the tension for low-income students in particular, who have to weigh this tradeoff earlier in their medical school journey than other students who are less financially-constrained (insight: “Medical school is a risky investment for low-income students”.) The design opportunity logically translates into a call to action for brainstorming and solution development: “Support low-income students to make values-based tradeoffs when considering a career in medicine.”
A total of 3,162 students responded to the MSWS and their sociodemographic characteristics have been described previously [ 3 ]. A total of 2,771 respondents (87.6%) responded to our study’s variables of interest, including a response for ‘high debt stress’ (–2) or ‘low debt stress’ (–1 or 0). Table 3 lists the distribution of debt-related stress across different variables for all respondents.
Factors that were independently associated with higher debt-related stress included being in pre-clinical year (OR 1.75, 95% CI 1.30–2.36, p < 0.001), identifying as URM (OR 1.40, 95% CI 1.03–1.88), p = 0.029), having debt $20–100 K (OR 4.85, 95% CI 3.32–7.30, p < 0.001), debt > 100 K (OR 13.22, 95% CI 9.05–19.90, p < 0.001), attending a private medical school (OR 1.45, 95% CI 1.06–1.98, p = 0.019), attending medical school on the West Coast (OR 1.57, 95% CI 1.17–2.13, p = 0.003), and having considered taking a leave of absence for wellbeing (OR 1.48, 95% CI 1.13–1.93, p = 0.004) (Table 4 , S1 Table).
Levels of generalized severe distress differed across debt burden groups. As debt level increased, the percentage of individuals with “severe” distress increased ( p < 0.001).
There were significant differences between the high debt stress versus low debt stress groups and plans to pursue highly vs. moderately vs. minimally competitive specialties ( p = 0.027) (Fig. 1 ) A greater percentage of low debt stress students were pursuing a highly competitive specialty or a minimally competitive specialty. A greater percentage of high debt stress students were pursuing a moderately competitive specialty. As shown in Table 4 , there were no differences in debt-associated stress between students who choose different specialties, such as medical versus surgical versus mixed (medical/surgical).
Debt stress by specialty competitiveness
URM identity was an independent risk factor for higher debt-related stress (Table 4 ) In addition, debt levels varied between those who identify as URM versus non-URM ( p < 0.001). Students identifying as URM tended to have higher debt than those who did not. Although the percentage of non-URM students was higher than that of URM students within the lowest debt burden category (<$20k), among all higher debt burden categories, including $20–100 K, $100–300 K, and >$300K, the percentage of URM students was higher than the percentage of non-URM students.
Protective factors such as high degree of mental health resource utilization and meeting with a counselor did not reduce the impact of debt burden on debt stress. Among students who reported a high degree of mental health resource utilization, there was no impact on the relationship between debt and debt stress ( p = 0.968). Similarly, meeting with a counselor had no impact on the relationship between debt and debt stress ( p = 0.640).
We conducted in-depth, semi-structured interviews with 11 medical students, who are briefly described in Table 2 . We reached thematic saturation with 11 interviews, a point at which we found recurring themes. Therefore, no further interviews were needed. Among the medical student interviewees, there was representation from all regions, including the Northeast ( n = 3), West Coast ( n = 5), Midwest ( n = 2), and South ( n = 1). Students were also from all clinical phases, including pre-clinical ( n = 3), clinical ( n = 4), gap year/other ( n = 2), and post-clinical ( n = 2). Most interviewees were female ( n = 8) and 5 of the interviewees identified as URM. Financial support systems were diverse, including self ( n = 3), spouse/partner ( n = 3), and parents/other ( n = 5). Most interviewees reported low debt stress ( n = 8), as opposed to high debt stress ( n = 3). 55% of interviewees planned to pursue specialties that pay <$300K ( n = 6), with some pursuing specialties that pay $300–400 K ( n = 2) and >$400K ( n = 3).
Among the MSWS free-text responses, to the prompt, “What well-being resource(s), if offered at your school, do you feel would be most useful?” 20 of 118 respondents (16.9%) provided free-text responses that pertained to debt, cost of medical school, or finances. To the prompt “If you have any further comments to share, please write them below” 11 of 342 students (3.2%) provided relevant free-text responses. Analysis of the free-text responses and semi-structured interviews revealed 6 distinct insights (Table 5 ), with each insight translated into an actionable design opportunity.
Medical school is a risky investment for low-income students.
The personal and financial sacrifices required for low-income students to attend medical school and pursue a career in medicine outweigh the benefits of becoming a physician. When considering a career in medicine, students feel discouraged by questionable job security (theme 1a) and reduced financial compensation (theme 1b) – a combination that jeopardizes immediate and long-term job satisfaction. Some students feel hopeful that their decision to pursue medicine will be personally rewarding (1b.6) and their salaries will stabilize (1a.1, 1a.5), but many low-income students experience doubt about whether they made the right career choice (1b.2, 1b.4, 1b.6), and feel stressed that they will be in debt for longer than they expected (1a.3, 1a.4, 1b.1, 1b.5). Support low-income students to make values-based tradeoffs when considering a career in medicine.
Support low-income students to make values-based tradeoffs when considering a career in medicine.
Medical schools lack the adaptive infrastructure to be welcoming to low-income students.
Students face financial challenges from the moment they apply to medical school (theme 2a), a costly process that limits admissions options for low-income students due to their inability to pay for numerous application fees (2a.1) and expensive test preparation courses (2a.2, 2a.3). Once students begin medical school, they feel unsupported in their varied responsibilities towards their families (theme 2b) and additional financial needs (theme 2c), requiring them to make tradeoffs with their education and personal lives (2b.2, 2c.1).
Develop flexible systems that can recognize and accommodate students’ complex financial needs during medical school.
Students worry about the impact that their medical school debt has on their present and future families, which compounds feelings of guilt and anxiety.
For students who need to take loans, the decision to pursue a career in medicine is a collective investment with their families. Students feel guilty about the sacrifices their families have to make for the sake of their career (theme 3a) and feel pressure to continue to provide financially for their family while having debt (theme 3b). Students are stressed about acquiring more debt throughout their training (3a.1) and the impact that has on loved ones who are dependent on them (3a.4, 3a.5, 3b.2), especially with respect to ensuring their financial security in the future (3b.4).
Create an environment that acknowledges and accounts for the burden of responsibility that students face towards their families.
Without the appropriate education about loans, the stress of debt is exponentially worse.
Students feel the greatest fear around loans when they do not understand them, including the process of securing loans and paying off debt (theme 4a). Students are overwhelmed by their loan amounts (4a.5) and lack the knowledge or resources to manage their debt (4a.1, 4a.2), making them uncertain about how they will become debt-free in the future (4a.3, 4a.4). Students reported that various resources helped to alleviate those burgeoning fears (theme 4b), including financial aid counselors (4b.2, 4b.3) and physician role models (4b.5, 4b.6) that generally increase knowledge and skills related to debt management (4b.1).
Empower students to become experts in managing their debt by making loan-related resources more available and accessible.
The small, daily expenses are the most burdensome and cause the greatest amount of stress.
Students with educational debt are mentally unprepared for the burden of managing their daily living expenses (theme 5a), causing them to make significant lifestyle adjustments in the hopes to ease their resulting anxiety (theme 5b). These costs are immediate and tangible, compared to tuition costs which are more distant and require less frequent management (5a.3) Students learn to temper their expectations for living beyond a bare minimum during medical school (5a.1, 5b.2, 5b.4) and develop strategies to ensure that their necessary expenses are as low as possible (5b.1, 5b.2, 5b.3, 5b.4).
Develop and distribute resources to support both short- and long-term financial costs for medical students.
Students view debt as a dark cloud that constrains their mental health and dictates their career trajectory.
The constant burden of educational debt constrains students’ abilities to control their mental health (theme 6a) and pursue their desired career path in medicine (themes 6b & 6c). Students feel controlled by their debt (6a.3) and concerned that it will impact their [ability] to live a personally fulfilling life (6a.1, 6a.2, 6c.6), especially with respect to pursuing their desired medical specialties (6b.1, 6c.3, 6c.5, 6c.6). Students with scholarships, as opposed to loans, felt more able to choose specialties that prioritized their values rather than their finances (6c.1, 6c.2), an affordance that impacts long-term career growth and satisfaction.
Create a culture of confidence for managing debt and debt-stress among medical students.
This is the first multi-institutional national study to explore the impact of debt-related stress on medical students’ well-being in the United States. We used an innovative, mixed methods approach to better understand the factors that significantly affect debt-related stress, and propose opportunities for improving medical student well-being.
Analysis of survey results found that students who identify as URM are more likely to experience higher levels of debt-related stress than non-URM students. Our study also found that among all higher debt burden categories, debt levels were higher for URM students, findings consistent with studies that have shown the disproportionate burden of debt among URM students [ 1 ]. Our semi-structured interviews illuminated that students from low-income backgrounds feel unsupported by their medical schools in these varied financial stressors that extend beyond tuition costs (insight 2), leaving their needs unmet and increasing financial stress over time: “We don’t have different socio-economic classes in medicine because there’s constantly a cost that [isn’t] even factored into tuition cost [and] that we can’t take student loans for.” Many URM students feel especially stressed by their financial obligations towards their families (insight 3), and describe the decision to enter into medicine as one that is collective ( “the family’s going to school” ) rather than individual, placing additional pressure on themselves to succeed in their career: “ Being of low SES , the most significant stressor for me is the financing of medical school and the pull of responsibility for my family.” Several other studies from the literature confirm that students who identify as URM and first generation college or medical students are at higher risk for financial stress compared to their counterparts [ 7 ], and report that they feel as though it is their responsibility to honor their families through their educational and career pursuits [ 25 ]. Our study demonstrates and describes how low-income and URM students face numerous financial barriers in medical school, resulting in medical trainees that are less diverse than the patient populations they are serving [ 1 , 8 ].
Our quantitative analysis found that students with debt amounts over $100,000 are at much higher risk for experiencing severe stress than students with debt less than that amount. Although this finding may seem intuitive, it is important to highlight the degree to which this risk differs between these two cohorts. Students with debt amounts between $20,000 and $100,000 are approximately 5 times more likely to experience high stress than students with debt less than $20,000, while students with debt amounts over $100,000 are approximately 13 times more likely to experience severe stress when compared to the same cohort. Interview participants describe that the more debt they have, the less hopeful they feel towards achieving financial security (insight 1): “There are other healthcare professionals that will not accrue the same amount of loans that we will , and then may or may not have the same salary or privileges […] makes me question , did I do the right thing?” Students internalize this rising stress so as not to shift the feelings of guilt onto their families (insight 3), thereby compounding the psychological burden associated with large amounts of debt (insight 6): “As long as you’re in debt , you’re owned by someone or something and the sooner you can get out of it , the better; the sooner I can get started with my life.”
According to our survey analysis, students who are in their pre-clinical years are at higher risk for stress than students in their clinical years. Our interview findings from insight 4 suggest that students feel initially overwhelmed and unsure about what questions to ask ( “One of my fears is that I don’t know what I don’t know”) or how to manage their loans so that it doesn’t have a permanent impact on their lives: “The biggest worry is , what if [the debt] becomes so large that I am never able to pay it off and it ends up ruining me financially.” Pre-clinical students may therefore feel unsure or ill-equipped to manage their loans, making them feel overwhelmed by the initial stimulus of debt. By the time students reach their clinical years, they may have had time to develop strategies for managing stress, acquire more financial knowledge, and/or normalize the idea of having debt.
Our survey analysis found several risk factors related to medical school characteristics. First, we found that students who attended a private school were at higher risk for debt-related stress than students who attended a public school. Not only is the median 4-year cost of attendance in 2023 almost $100,000 higher in private compared to public medical schools [ 26 ], but it is also the case that financial aid packages are more liberally available for public schools due to state government funding [ 27 ]. This not only relieves students from having higher amounts of debt, but it also creates a more inclusive cohort of medical students. Insight 2 from our interviews suggests that private medical schools without the infrastructure to meet students’ varying financial needs force low-income students to make tradeoffs between their education and personal lives.
Another characteristic that was found to be a risk factor for debt stress was attending a medical school on the West Coast (compared to a non-coastal school.) This was a surprising finding given that tuition rates for both private and public schools on the West Coast are no higher than those in other regions [ 17 ]. The distribution of survey respondents did not vary significantly across regional categories, so no bias in sample size is suspected. While these interviews were not designed to address the reasoning behind students’ choice of medical school matriculation, there is a potential explanation for this finding. Historically, students match for residency programs that are in their home state or not far from their home state; [ 28 , 29 ] therefore, we speculate that students may prefer to settle on the West Coast, and may be willing to take on more financial debt in pursuit of their long-term practice and lifestyle goals.
Our quantitative analysis found that students who reported having considered taking a leave of absence for well-being purposes were at higher risk for debt-related stress. This cohort of students likely experience higher levels of stress as they are conscious of the negative impact it has on their life, and have already ruminated on leaving medical school. A study by Fallar et al. found that the period leading up to a leave of absence is particularly stressful for students because they are unfamiliar with the logistics of taking time off, and don’t feel as though leaving medical school is encouraged or normalized for students [ 30 ]. An interview with a student who did a joint MD and PhD program expressed having more time for herself during her PhD program, and described using money for activities that could alleviate stress (“I took figure skating during my PhD”) rather than create more stress by compromising on their lifestyle during medical school (insight 5). More research may be needed to better understand and support students considering taking a leave of absence from medical school.
Our study found that students with high debt stress pursue moderately competitive specialties compared to students with low debt stress. This may be explained by the fact that low debt stress gives students the freedom to pursue minimally competitive specialties, which may be more fulfilling to them but typically have lower salaries. Insight 6 further elaborates upon this finding that students with high debt stress deprioritize specialties for which they are passionate in favor of higher paying specialties that might alleviate their debt: “I love working with kids…but being an outpatient pediatrician just wasn’t going to be enough to justify the [private school] price tag.” Students with lower debt stress describe having the freedom to choose specialties that align with their values, regardless of anticipated salary: “Scholarships give me the freedom to do [specialties] that maybe are a little bit less well-paying in medicine.” Interestingly, certain studies examining the relationship between specialty choice and debt stress have found that high debt stress is associated with a higher likelihood of pursuing a more competitive, and presumably higher paying, specialty [ 5 ]. More research investigating the relationship between debt stress and specialty choice could illuminate opportunities for increasing a sense of agency and overall satisfaction among students for their career choices.
In our exploration of potential protective factors against the effects of debt-related stress, our survey analysis found that the two variables measured (high mental health resource utilization and meeting with a counselor) did not have any impact on reducing debt-related stress. This finding is inconsistent with the literature, which considers these activities to promote general well-being among students but has never been studied in the context of debt-related stress [ 13 , 14 , 15 ]. A potential explanation is that the survey questions that assessed these activities were imperfect. For example, the question of meeting with a counselor was not a standalone question, but instead, was at the bottom of a list of other wellbeing activities; therefore, students may have been fatigued by the time they got to the bottom of the list and not selected it. Additionally, our definition of “high” mental health resource utilization may have been perceived as too strict (i.e.: 80–100%) and perhaps we would have seen effects at lower percentages of utilization (i.e.: 40–60%). Despite this finding, students describe in their interviews that having access to certain resources such as financial knowledge and physician role models can help to alleviate stress by helping them feel confident in managing their loans in the immediate and more distant future (insight 4): “I’ve had explicit discussions with physicians who went to med school , had debt , paid it off [.] the debt hasn’t hindered their life in any way. I think that just makes me feel a lot calmer.” This finding aligns with previous studies that suggest that financial knowledge, such as knowledge about loans and a payoff plan, confers confidence in students’ financial management [ 11 , 12 ]. These factors are also aligned with previous studies that suggest financial optimism, such as with a physician role model who successfully paid off loans, is associated with less financial stress [ 10 ].
Our quantitative analysis of risk factors helped us to identify which areas might significantly impact debt-related stress among medical students, while our qualitative analysis provided more in-depth insight into those risk factors for more human-centered intervention design. The HCD process not only provides additional context from the perspective of medical students, but also proposes distinct design opportunities upon which interventions may be designed and tested. Drawing from the six design opportunities outlined in this paper, we propose a solution on a national scale: lowering the cost of the MCAT and medical school applications to reduce the financial barrier to applying to medical school [ 31 ]. We also propose the following solutions that can be implemented at the level of medical schools to better support medical students facing debt-related stress: (1) providing adequate financial aid that prevents low-income students from needing to work while being in medical school [ 32 ], (2) providing targeted financial planning classes and counseling for first-year medical students who have taken loans [ 33 ], and (3) creating mentorship programs that pair medical students with debt with physician role models who had also had debt but successfully paid it off [ 34 ]. We encourage medical schools to consider these suggestions, choosing the ideas from the list that make sense and tailoring them as necessary for their students and their unique needs. Additionally, given that our quantitative portion of the study was a secondary analysis of a survey focused on general medical student well-being, a nationwide study is needed that is specifically designed to explore the topic of debt-related stress among medical students. Furthermore, more research is needed that assesses the impact of activities that promote well-being (e.g., access to therapy, mindfulness practices, exercise) on debt-related stress among medical students.
Our study had some notable limitations. One potential limitation is that our data collection occurred between 2019 and 2021 for this publication in 2023. Additionally, as described in the original study [ 3 ], a limitation of the MSWS is the inability to determine a response rate of students due to the survey distribution by medical student liaisons from each medical school; under the reasonable assumption that the survey was distributed to every US allopathic medical student, the response rate was estimated to have been 8.7%. 3 An additional limitation is the potential for response bias [ 3 ]. A limitation of the qualitative interviews is the potential for response bias among the interviewees. Although we purposely sampled, the students who accepted the invitation to interview may have been students with extreme views, either very negative views of debt or very neutral views of debt. Additionally, the interviewees were not representative of all possible financial situations, given that most students were from private schools, which typically have higher tuition rates. Also, all students had debt amounts in the middle and high categories, with none in the low category. Finally, our model of risk factors for debt-related stress suggested the presence of negative confounding factors, which exerted effects on specific variables (i.e.: pre-clinical year, West Coast) for which univariate analysis found no significant associations but multivariate analysis did. We did not perform further analysis to identify which variables served as the negative confounding variables.
In conclusion, our mixed methods, cross-sectional study exploring debt-related stress and its impact on US medical students’ wellbeing and professional development revealed a set of risk factors and design opportunities for intervention. By using a combined quantitative and qualitative HCD approach, we were able to develop a broad, in-depth understanding of the challenges and opportunities facing medical students with education debt. With these efforts to support the well-being and academic success of students at higher risk of debt-related stress, medical education institutions can develop and nurture a more diverse medical field that can best support the needs of future patients.
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We thank the members of The Better Lab, including Devika Patel, Christiana Von Hippel, and Marianna Salvatori, for their support. We appreciate Pamela Derish (UCSF) for assistance in manuscript editing and the UCSF Clinical and Translational Science Institute (CTSI) for assistance in statistical analysis. This publication was supported by the National Center for Advancing Translational Sciences, National Institutes of Health, through UCSF-CTSI Grant Number UL1 TR001872. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH.
Funding was not obtained for this project.
Adrienne Yang, Simone Langness and Lara Chehab contributed equally to this work.
Department of Surgery, University of California, San Francisco, CA, USA
Adrienne Yang, Lara Chehab & Amanda Sammann
Department of Trauma Surgery, Sharp HealthCare, San Diego, CA, USA
Simone Langness
Department of Pediatrics, Stanford University, Stanford, CA, USA
Nikhil Rajapuram
Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
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A.Y. and L.C. wrote the main manuscript text and prepared the figures. S.L. created the study design. All authors reviewed the manuscript.
Correspondence to Adrienne Yang .
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Yang, A., Langness, S., Chehab, L. et al. Medical students in distress: a mixed methods approach to understanding the impact of debt on well-being. BMC Med Educ 24 , 947 (2024). https://doi.org/10.1186/s12909-024-05927-9
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