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• v.60(9); 2016 Sep

Basic statistical tools in research and data analysis

Zulfiqar ali.

Department of Anaesthesiology, Division of Neuroanaesthesiology, Sheri Kashmir Institute of Medical Sciences, Soura, Srinagar, Jammu and Kashmir, India

S Bala Bhaskar

1 Department of Anaesthesiology and Critical Care, Vijayanagar Institute of Medical Sciences, Bellary, Karnataka, India

Statistical methods involved in carrying out a study include planning, designing, collecting data, analysing, drawing meaningful interpretation and reporting of the research findings. The statistical analysis gives meaning to the meaningless numbers, thereby breathing life into a lifeless data. The results and inferences are precise only if proper statistical tests are used. This article will try to acquaint the reader with the basic research tools that are utilised while conducting various studies. The article covers a brief outline of the variables, an understanding of quantitative and qualitative variables and the measures of central tendency. An idea of the sample size estimation, power analysis and the statistical errors is given. Finally, there is a summary of parametric and non-parametric tests used for data analysis.

INTRODUCTION

Statistics is a branch of science that deals with the collection, organisation, analysis of data and drawing of inferences from the samples to the whole population.[ 1 ] This requires a proper design of the study, an appropriate selection of the study sample and choice of a suitable statistical test. An adequate knowledge of statistics is necessary for proper designing of an epidemiological study or a clinical trial. Improper statistical methods may result in erroneous conclusions which may lead to unethical practice.[ 2 ]

Variable is a characteristic that varies from one individual member of population to another individual.[ 3 ] Variables such as height and weight are measured by some type of scale, convey quantitative information and are called as quantitative variables. Sex and eye colour give qualitative information and are called as qualitative variables[ 3 ] [ Figure 1 ].

Classification of variables

Quantitative variables

Quantitative or numerical data are subdivided into discrete and continuous measurements. Discrete numerical data are recorded as a whole number such as 0, 1, 2, 3,… (integer), whereas continuous data can assume any value. Observations that can be counted constitute the discrete data and observations that can be measured constitute the continuous data. Examples of discrete data are number of episodes of respiratory arrests or the number of re-intubations in an intensive care unit. Similarly, examples of continuous data are the serial serum glucose levels, partial pressure of oxygen in arterial blood and the oesophageal temperature.

A hierarchical scale of increasing precision can be used for observing and recording the data which is based on categorical, ordinal, interval and ratio scales [ Figure 1 ].

Categorical or nominal variables are unordered. The data are merely classified into categories and cannot be arranged in any particular order. If only two categories exist (as in gender male and female), it is called as a dichotomous (or binary) data. The various causes of re-intubation in an intensive care unit due to upper airway obstruction, impaired clearance of secretions, hypoxemia, hypercapnia, pulmonary oedema and neurological impairment are examples of categorical variables.

Ordinal variables have a clear ordering between the variables. However, the ordered data may not have equal intervals. Examples are the American Society of Anesthesiologists status or Richmond agitation-sedation scale.

Interval variables are similar to an ordinal variable, except that the intervals between the values of the interval variable are equally spaced. A good example of an interval scale is the Fahrenheit degree scale used to measure temperature. With the Fahrenheit scale, the difference between 70° and 75° is equal to the difference between 80° and 85°: The units of measurement are equal throughout the full range of the scale.

Ratio scales are similar to interval scales, in that equal differences between scale values have equal quantitative meaning. However, ratio scales also have a true zero point, which gives them an additional property. For example, the system of centimetres is an example of a ratio scale. There is a true zero point and the value of 0 cm means a complete absence of length. The thyromental distance of 6 cm in an adult may be twice that of a child in whom it may be 3 cm.

STATISTICS: DESCRIPTIVE AND INFERENTIAL STATISTICS

Descriptive statistics[ 4 ] try to describe the relationship between variables in a sample or population. Descriptive statistics provide a summary of data in the form of mean, median and mode. Inferential statistics[ 4 ] use a random sample of data taken from a population to describe and make inferences about the whole population. It is valuable when it is not possible to examine each member of an entire population. The examples if descriptive and inferential statistics are illustrated in Table 1 .

Example of descriptive and inferential statistics

Descriptive statistics

The extent to which the observations cluster around a central location is described by the central tendency and the spread towards the extremes is described by the degree of dispersion.

Measures of central tendency

The measures of central tendency are mean, median and mode.[ 6 ] Mean (or the arithmetic average) is the sum of all the scores divided by the number of scores. Mean may be influenced profoundly by the extreme variables. For example, the average stay of organophosphorus poisoning patients in ICU may be influenced by a single patient who stays in ICU for around 5 months because of septicaemia. The extreme values are called outliers. The formula for the mean is

where x = each observation and n = number of observations. Median[ 6 ] is defined as the middle of a distribution in a ranked data (with half of the variables in the sample above and half below the median value) while mode is the most frequently occurring variable in a distribution. Range defines the spread, or variability, of a sample.[ 7 ] It is described by the minimum and maximum values of the variables. If we rank the data and after ranking, group the observations into percentiles, we can get better information of the pattern of spread of the variables. In percentiles, we rank the observations into 100 equal parts. We can then describe 25%, 50%, 75% or any other percentile amount. The median is the 50 th percentile. The interquartile range will be the observations in the middle 50% of the observations about the median (25 th -75 th percentile). Variance[ 7 ] is a measure of how spread out is the distribution. It gives an indication of how close an individual observation clusters about the mean value. The variance of a population is defined by the following formula:

where σ 2 is the population variance, X is the population mean, X i is the i th element from the population and N is the number of elements in the population. The variance of a sample is defined by slightly different formula:

where s 2 is the sample variance, x is the sample mean, x i is the i th element from the sample and n is the number of elements in the sample. The formula for the variance of a population has the value ‘ n ’ as the denominator. The expression ‘ n −1’ is known as the degrees of freedom and is one less than the number of parameters. Each observation is free to vary, except the last one which must be a defined value. The variance is measured in squared units. To make the interpretation of the data simple and to retain the basic unit of observation, the square root of variance is used. The square root of the variance is the standard deviation (SD).[ 8 ] The SD of a population is defined by the following formula:

where σ is the population SD, X is the population mean, X i is the i th element from the population and N is the number of elements in the population. The SD of a sample is defined by slightly different formula:

where s is the sample SD, x is the sample mean, x i is the i th element from the sample and n is the number of elements in the sample. An example for calculation of variation and SD is illustrated in Table 2 .

Example of mean, variance, standard deviation

Normal distribution or Gaussian distribution

Most of the biological variables usually cluster around a central value, with symmetrical positive and negative deviations about this point.[ 1 ] The standard normal distribution curve is a symmetrical bell-shaped. In a normal distribution curve, about 68% of the scores are within 1 SD of the mean. Around 95% of the scores are within 2 SDs of the mean and 99% within 3 SDs of the mean [ Figure 2 ].

Normal distribution curve

Skewed distribution

It is a distribution with an asymmetry of the variables about its mean. In a negatively skewed distribution [ Figure 3 ], the mass of the distribution is concentrated on the right of Figure 1 . In a positively skewed distribution [ Figure 3 ], the mass of the distribution is concentrated on the left of the figure leading to a longer right tail.

Curves showing negatively skewed and positively skewed distribution

Inferential statistics

In inferential statistics, data are analysed from a sample to make inferences in the larger collection of the population. The purpose is to answer or test the hypotheses. A hypothesis (plural hypotheses) is a proposed explanation for a phenomenon. Hypothesis tests are thus procedures for making rational decisions about the reality of observed effects.

Probability is the measure of the likelihood that an event will occur. Probability is quantified as a number between 0 and 1 (where 0 indicates impossibility and 1 indicates certainty).

In inferential statistics, the term ‘null hypothesis’ ( H 0 ‘ H-naught ,’ ‘ H-null ’) denotes that there is no relationship (difference) between the population variables in question.[ 9 ]

Alternative hypothesis ( H 1 and H a ) denotes that a statement between the variables is expected to be true.[ 9 ]

The P value (or the calculated probability) is the probability of the event occurring by chance if the null hypothesis is true. The P value is a numerical between 0 and 1 and is interpreted by researchers in deciding whether to reject or retain the null hypothesis [ Table 3 ].

P values with interpretation

If P value is less than the arbitrarily chosen value (known as α or the significance level), the null hypothesis (H0) is rejected [ Table 4 ]. However, if null hypotheses (H0) is incorrectly rejected, this is known as a Type I error.[ 11 ] Further details regarding alpha error, beta error and sample size calculation and factors influencing them are dealt with in another section of this issue by Das S et al .[ 12 ]

Illustration for null hypothesis

PARAMETRIC AND NON-PARAMETRIC TESTS

Numerical data (quantitative variables) that are normally distributed are analysed with parametric tests.[ 13 ]

Two most basic prerequisites for parametric statistical analysis are:

• The assumption of normality which specifies that the means of the sample group are normally distributed
• The assumption of equal variance which specifies that the variances of the samples and of their corresponding population are equal.

However, if the distribution of the sample is skewed towards one side or the distribution is unknown due to the small sample size, non-parametric[ 14 ] statistical techniques are used. Non-parametric tests are used to analyse ordinal and categorical data.

Parametric tests

The parametric tests assume that the data are on a quantitative (numerical) scale, with a normal distribution of the underlying population. The samples have the same variance (homogeneity of variances). The samples are randomly drawn from the population, and the observations within a group are independent of each other. The commonly used parametric tests are the Student's t -test, analysis of variance (ANOVA) and repeated measures ANOVA.

Student's t -test

Student's t -test is used to test the null hypothesis that there is no difference between the means of the two groups. It is used in three circumstances:

where X = sample mean, u = population mean and SE = standard error of mean

where X 1 − X 2 is the difference between the means of the two groups and SE denotes the standard error of the difference.

• To test if the population means estimated by two dependent samples differ significantly (the paired t -test). A usual setting for paired t -test is when measurements are made on the same subjects before and after a treatment.

The formula for paired t -test is:

where d is the mean difference and SE denotes the standard error of this difference.

The group variances can be compared using the F -test. The F -test is the ratio of variances (var l/var 2). If F differs significantly from 1.0, then it is concluded that the group variances differ significantly.

Analysis of variance

The Student's t -test cannot be used for comparison of three or more groups. The purpose of ANOVA is to test if there is any significant difference between the means of two or more groups.

In ANOVA, we study two variances – (a) between-group variability and (b) within-group variability. The within-group variability (error variance) is the variation that cannot be accounted for in the study design. It is based on random differences present in our samples.

However, the between-group (or effect variance) is the result of our treatment. These two estimates of variances are compared using the F-test.

A simplified formula for the F statistic is:

where MS b is the mean squares between the groups and MS w is the mean squares within groups.

Repeated measures analysis of variance

As with ANOVA, repeated measures ANOVA analyses the equality of means of three or more groups. However, a repeated measure ANOVA is used when all variables of a sample are measured under different conditions or at different points in time.

As the variables are measured from a sample at different points of time, the measurement of the dependent variable is repeated. Using a standard ANOVA in this case is not appropriate because it fails to model the correlation between the repeated measures: The data violate the ANOVA assumption of independence. Hence, in the measurement of repeated dependent variables, repeated measures ANOVA should be used.

Non-parametric tests

When the assumptions of normality are not met, and the sample means are not normally, distributed parametric tests can lead to erroneous results. Non-parametric tests (distribution-free test) are used in such situation as they do not require the normality assumption.[ 15 ] Non-parametric tests may fail to detect a significant difference when compared with a parametric test. That is, they usually have less power.

As is done for the parametric tests, the test statistic is compared with known values for the sampling distribution of that statistic and the null hypothesis is accepted or rejected. The types of non-parametric analysis techniques and the corresponding parametric analysis techniques are delineated in Table 5 .

Analogue of parametric and non-parametric tests

Median test for one sample: The sign test and Wilcoxon's signed rank test

The sign test and Wilcoxon's signed rank test are used for median tests of one sample. These tests examine whether one instance of sample data is greater or smaller than the median reference value.

This test examines the hypothesis about the median θ0 of a population. It tests the null hypothesis H0 = θ0. When the observed value (Xi) is greater than the reference value (θ0), it is marked as+. If the observed value is smaller than the reference value, it is marked as − sign. If the observed value is equal to the reference value (θ0), it is eliminated from the sample.

If the null hypothesis is true, there will be an equal number of + signs and − signs.

The sign test ignores the actual values of the data and only uses + or − signs. Therefore, it is useful when it is difficult to measure the values.

Wilcoxon's signed rank test

There is a major limitation of sign test as we lose the quantitative information of the given data and merely use the + or – signs. Wilcoxon's signed rank test not only examines the observed values in comparison with θ0 but also takes into consideration the relative sizes, adding more statistical power to the test. As in the sign test, if there is an observed value that is equal to the reference value θ0, this observed value is eliminated from the sample.

Wilcoxon's rank sum test ranks all data points in order, calculates the rank sum of each sample and compares the difference in the rank sums.

Mann-Whitney test

It is used to test the null hypothesis that two samples have the same median or, alternatively, whether observations in one sample tend to be larger than observations in the other.

Mann–Whitney test compares all data (xi) belonging to the X group and all data (yi) belonging to the Y group and calculates the probability of xi being greater than yi: P (xi > yi). The null hypothesis states that P (xi > yi) = P (xi < yi) =1/2 while the alternative hypothesis states that P (xi > yi) ≠1/2.

Kolmogorov-Smirnov test

The two-sample Kolmogorov-Smirnov (KS) test was designed as a generic method to test whether two random samples are drawn from the same distribution. The null hypothesis of the KS test is that both distributions are identical. The statistic of the KS test is a distance between the two empirical distributions, computed as the maximum absolute difference between their cumulative curves.

Kruskal-Wallis test

The Kruskal–Wallis test is a non-parametric test to analyse the variance.[ 14 ] It analyses if there is any difference in the median values of three or more independent samples. The data values are ranked in an increasing order, and the rank sums calculated followed by calculation of the test statistic.

Jonckheere test

In contrast to Kruskal–Wallis test, in Jonckheere test, there is an a priori ordering that gives it a more statistical power than the Kruskal–Wallis test.[ 14 ]

Friedman test

The Friedman test is a non-parametric test for testing the difference between several related samples. The Friedman test is an alternative for repeated measures ANOVAs which is used when the same parameter has been measured under different conditions on the same subjects.[ 13 ]

Tests to analyse the categorical data

Chi-square test, Fischer's exact test and McNemar's test are used to analyse the categorical or nominal variables. The Chi-square test compares the frequencies and tests whether the observed data differ significantly from that of the expected data if there were no differences between groups (i.e., the null hypothesis). It is calculated by the sum of the squared difference between observed ( O ) and the expected ( E ) data (or the deviation, d ) divided by the expected data by the following formula:

A Yates correction factor is used when the sample size is small. Fischer's exact test is used to determine if there are non-random associations between two categorical variables. It does not assume random sampling, and instead of referring a calculated statistic to a sampling distribution, it calculates an exact probability. McNemar's test is used for paired nominal data. It is applied to 2 × 2 table with paired-dependent samples. It is used to determine whether the row and column frequencies are equal (that is, whether there is ‘marginal homogeneity’). The null hypothesis is that the paired proportions are equal. The Mantel-Haenszel Chi-square test is a multivariate test as it analyses multiple grouping variables. It stratifies according to the nominated confounding variables and identifies any that affects the primary outcome variable. If the outcome variable is dichotomous, then logistic regression is used.

SOFTWARES AVAILABLE FOR STATISTICS, SAMPLE SIZE CALCULATION AND POWER ANALYSIS

Numerous statistical software systems are available currently. The commonly used software systems are Statistical Package for the Social Sciences (SPSS – manufactured by IBM corporation), Statistical Analysis System ((SAS – developed by SAS Institute North Carolina, United States of America), R (designed by Ross Ihaka and Robert Gentleman from R core team), Minitab (developed by Minitab Inc), Stata (developed by StataCorp) and the MS Excel (developed by Microsoft).

There are a number of web resources which are related to statistical power analyses. A few are:

• StatPages.net – provides links to a number of online power calculators
• G-Power – provides a downloadable power analysis program that runs under DOS
• Power analysis for ANOVA designs an interactive site that calculates power or sample size needed to attain a given power for one effect in a factorial ANOVA design
• SPSS makes a program called SamplePower. It gives an output of a complete report on the computer screen which can be cut and paste into another document.

It is important that a researcher knows the concepts of the basic statistical methods used for conduct of a research study. This will help to conduct an appropriately well-designed study leading to valid and reliable results. Inappropriate use of statistical techniques may lead to faulty conclusions, inducing errors and undermining the significance of the article. Bad statistics may lead to bad research, and bad research may lead to unethical practice. Hence, an adequate knowledge of statistics and the appropriate use of statistical tests are important. An appropriate knowledge about the basic statistical methods will go a long way in improving the research designs and producing quality medical research which can be utilised for formulating the evidence-based guidelines.

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Title: Auditing the Fairness of COVID-19 Forecast Hub Case Prediction Models

Abstract: The COVID-19 Forecast Hub, a repository of COVID-19 forecasts from over 50 independent research groups, is used by the Centers for Disease Control and Prevention (CDC) for their official COVID-19 communications. As such, the Forecast Hub is a critical centralized resource to promote transparent decision making. Nevertheless, by focusing exclusively on prediction accuracy, the Forecast Hub fails to evaluate whether the proposed models have similar performance across social determinants that have been known to play a role in the COVID-19 pandemic including race, ethnicity and urbanization level. In this paper, we carry out a comprehensive fairness analysis of the Forecast Hub model predictions and we show statistically significant diverse predictive performance across social determinants, with minority racial and ethnic groups as well as less urbanized areas often associated with higher prediction errors. We hope this work will encourage COVID-19 modelers and the CDC to report fairness metrics together with accuracy, and to reflect on the potential harms of the models on specific social groups and contexts.

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• Epistemic injustice, healthcare disparities and the missing pipeline: reflections on the exclusion of disabled scholars from health research
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• http://orcid.org/0000-0003-3868-5765 Joanne Hunt 1 ,
• http://orcid.org/0000-0002-0205-1165 Charlotte Blease 1 , 2
• 1 Department of Women's and Children's Health , Uppsala University , Uppsala , Sweden
• 2 Digital Psychiatry, Department of Psychiatry, Beth Israel Deaconess Medical Center , Harvard Medical School , Boston , Massachusetts , USA
• Correspondence to Joanne Hunt, Department of Women's and Children's Health, Uppsala University, Uppsala 751 05, Sweden; joanne.hunt{at}uu.se

People with disabilities are subject to multiple forms of health-related and wider social disparities; carefully focused research is required to inform more inclusive, safe and effective healthcare practice and policy. Through lived experience, disabled people are well positioned to identify and persistently pursue problems and opportunities within existing health provisions that may be overlooked by a largely non-disabled research community. Thus, the academy can play an important role in shining a light on the perspectives and insights from within the disability community, and combined with policy decisions, these perspectives and insights have a better opportunity to become integrated into the fabric of public life, within healthcare and beyond. However, despite the potential benefits that could be yielded by greater inclusivity, in this paper we describe barriers within the UK academy confronting disabled people who wish to embark on health research. We do this by drawing on published findings, and via the lived experience of the first author, who has struggled for over 3 years to find an accessible PhD programme as a person with energy limiting conditions who is largely confined to the home in the UK. First, we situate the discussion in the wider perspective of epistemic injustice in health research. Second, we consider evidence of epistemic injustice among disabled researchers, focusing primarily on what philosophers Kidd and Carel (2017, p 184) describe as ‘strategies of exclusion’. Third, we offer recommendations for overcoming these barriers to improve the pipeline of researchers with disabilities in the academy.

• Disabled Persons
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Data availability statement

Data sharing not applicable as no datasets generated and/or analysed for this study.

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:  http://creativecommons.org/licenses/by-nc/4.0/ .

https://doi.org/10.1136/jme-2023-109837

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Introduction

People with disabilities have been described as an ‘unrecognized health disparity population’. 1 Health disparity (or health inequity) is understood as an avoidable and unjust difference in health or healthcare outcomes experienced by social, geographical or demographic groups with a history of socioeconomic, political or cultural discrimination and exclusion. 1 2 Despite the passage of landmark disability legislation, including the UK Equality Act 2010, the US Americans with Disabilities Act 1990 and the United Nations Convention on the Rights of Persons with Disabilities (adopted in 2006), disability-related health and healthcare disparities persist. Disabled people report lower levels of well-being on average compared with non-disabled people, are at increased risk of physical and mental comorbidity and are more likely to die younger. 1–3 There are multiple reasons as to why health disparities persist along the lines of disability; however, prejudicial biases, engendering structural barriers to care, play a critical part. For example, recently, the WHO 2 reported that people with disabilities are significantly more likely to perceive discrimination and stigma in healthcare contexts compared with non-disabled people. This is supported by a wealth of literature from across the world revealing institutional, physical and attitudinal healthcare barriers for disabled people, including medical professionals’ ambivalence or lack of understanding towards disability, lack of confidence vis-à-vis providing quality care and physically inaccessible clinics and clinical equipment. 4–7

Health and healthcare-related disparities also intersect with broader social disparities. For example, people with disabilities are less likely to be employed and earn less when they are in work, despite the fact that disability incurs higher living costs. 2 In the UK, government data from 2021 reveal a disability employment gap of 28%, 8 with a disability pay gap of 14%. 9 Recent figures from the US Bureau of Labor Statistics 10 indicate that the unemployment rate among disabled people is over twice the rate for non-disabled people, with similar trends across other countries. 2 Perhaps unsurprisingly, disabled people are also more likely to live in poverty than their non-disabled counterparts. 2 11 Compounding matters is structural disablism: discrimination and stigma (woven into collective attitudes, organisational policies, legislation and infrastructure) that often go unnoticed by non-disabled people but can take a serious toll on individuals living with disabilities. In 2023, the UK’s Office for National Statistics reported that the suicide rate was higher among people with disabilities than any other demographic group. 12

To better understand and address such disparities, carefully focused research is needed. 2 In this regard, people with lived experience of chronic illness and disability can offer unique insights that can strengthen and help drive richer research, where disabled people are positioned equally as co-researchers, as opposed to the traditional dynamic of disabled ‘research subject’ to be passively studied. Through first-hand experience, via experiential or standpoint epistemology, 13 disabled researchers are often well positioned to understand how health-related policies and practices (informed through largely non-disabled research communities) may unwittingly harm or otherwise disadvantage disabled persons. 14 Researchers with disabilities may also be more motivated and well placed to perceive knowledge gaps, and to pose penetrating and uncomfortable questions necessary to galvanise change. Embracing viewpoint diversity, and the input of disabled researchers, could therefore represent a powerful pathway to improve understanding and to develop more inclusive health and healthcare policy and practice.

The history of the disabled people’s movement within the UK, 15–17 whereby disabled scholar-activists entered the academy and contributed to profound changes in social practice and policy, constitutes an exemplar of the potential value of viewpoint diversity and disability standpoint, the legacy of which continues today, most notably within disability studies, but also more widely within critical social sciences and humanities. 18–20 However, within health sciences—particularly those tightly tied to science, technology, engineering and mathematics (STEM)—there appear to be greater barriers to including disabled scholars and integrating disabled knowledges. 21–23 For example, research shows that the percentage of people with a declared disability is lower in STEM subjects relative to non-STEM subjects at first degree, postgraduate level and within the academic workforce. 22 Moreover, a 2020 data analysis brief from the UK All-Party Parliamentary Group on Diversity and Inclusion in STEM 23 reported that the UK STEM workforce had a lower representation of disabled people relative to the rest of the UK workforce (11% vs 14%). Here, it is noteworthy that the analysis used the wider definition of STEM, that of ‘STEM(H)’ which specifically includes health and related fields. 23 Such exclusions are further compounded by intersectionality, the intersection and co-constitution of multiple forms of social (dis)advantage. 24 Indeed, the intersection of disability with other minoritised identities 19 21 23 is yet another reason to promote disability inclusion within the academy and beyond.

Despite the potential benefits that could be yielded by greater inclusivity, in this paper we describe barriers within the UK academy confronting disabled people who wish to embark on health research. We do this by drawing on published findings, and via the lived experience of the first author (hereafter, ‘JH’) who has struggled for over 3 years to find an accessible PhD programme in the UK as a person with ‘energy limiting conditions’ (ELC) 25 26 who is largely confined to the home. First, we situate the discussion in the wider perspective of epistemic injustice in health research. Second, we consider evidence of epistemic injustice among disabled researchers, in particular those with ELC, by situating this in the legal context in the UK, and by detailing the nature of barriers experienced. Third, we offer recommendations for overcoming these barriers in the academy.

A note on nomenclature: we recognise that person-first language (‘people with disabilities’) is the globally prevalent form. 18 As a self-identifying disabled person broadly ascribing to the British social model of disability, 16 17 JH tends towards identity-first language (‘disabled people’). Therefore, while recognising the semantic and ideological divergences embedded within different forms of disability-related language, 18 we have chosen to adopt both forms in this paper to reflect our case for viewpoint diversity.

Additionally, while recognising the heterogeneity of disability and disability-related exclusions, 19 we focus on ELC: health conditions that share energy impairment as a key experience and substrate of disability discrimination or disablism.

ELC include but are not limited to ‘medically unexplained’ or contested conditions such as myalgic encephalomyelitis/chronic fatigue syndrome, alongside ‘rare’ conditions such as Ehlers-Danlos syndromes. 25 26 Since ELC do not conform to socially prevalent (fixed, non-fluctuating, easily identifiable) stereotypes of disability, disablism largely manifests as clinical and social disbelief, resulting in ELC being poorly recognised and poorly researched through the lens of disability rights and diversity, equity and inclusion (DEI). 25 26 Equally, while we focus on exclusions within the academic space, it is important to note that people with ELC (and wider disabled communities) are subject to marginalisation and exclusion in all social arenas, including education, employment and the healthcare system itself. 25–28 Moreover, measures to improve physical inclusion (such as wheelchair-accessible environments) are oftentimes ineffective or insufficient among people with ELC who are confined to the home, thus furthering marginalisation of this group. In this respect, we recognise that people diagnosed with mental health conditions (notably but not limited to agoraphobia or social anxiety) may be confined to the home and are subject to similar dynamics of disability-related disbelief and associated exclusions as evidenced in the ELC arena. 29–31 Therefore, while we focus on ELC, the following discussion and recommendations for academic inclusion may benefit others with ‘hidden’ or poorly recognised health conditions.

The importance of ELC-specific research is arguably amplified by the emergence of long COVID, another condition that sits well within the ELC umbrella. 26 The concept of ELC arose from research led by disabled people within and outside of the UK academy 25 26 and thus represents an example of the potential value of ‘disability standpoint’ in contributing to health and healthcare-related research gaps. Nevertheless, there is very little peer-reviewed academic literature explicitly focusing on ELC (for recent exceptions see ref 32–34 ). To our knowledge, and motivating this paper, there is no research exploring academic exclusions in the ELC arena through a lens of epistemic injustice.

Epistemic injustice

Epistemic injustice refers to a variety of wrongs perpetrated against individuals in their capacity as a knower or contributor to knowledge. According to philosopher Miranda Fricker, 35 it takes two forms: testimonial injustice and hermeneutic injustice. The former arises when an individual is unfairly discriminated against with respect to their capacity to know or contribute to knowledge. This form of injustice often arises because of negative stereotypes about a demographic group. For example, in the case of disability, testimonial injustice may take the form of global, unjustified prejudices about the intellectual or bodily capacity of disabled individuals to contribute to knowledge. Disabled people may, for example, be seen as lacking the stamina, strength, reliability or acuity to offer useful insights. Philosophers of medicine Ian Kidd and Havi Carel 36 sum it up as a ‘pre-emptive derogation of the epistemic credibility and capacities of ill persons’ that involves ‘a prior view, for instance, of ill persons being confused, incapable or incompetent, that distorts an evaluation of their actual epistemic performance’. Testimonial injustice can take the form of implicit or explicit discrimination on the part of the hearer, leading to an outright dismissal or discrediting of the contribution of individuals to discussions in which they might otherwise offer valuable insights.

As others have argued, many people with disabilities may have acquired valuable knowledge about their condition through lived experience that renders them experts on aspects of their illness, the nature of health services and the quality of provider care. 27 37 38 Notwithstanding, it is also important to clarify that living with an illness need not automatically afford epistemic privilege. Rather, the point is that a finer awareness is needed to move past unhelpful stereotyping, to appreciate the contributions to knowledge that individuals may make. This, with a view to avoiding global or unwarranted assumptions about the credibility of individuals’ contributions to knowledge formation activities.

Hermeneutic injustice represents a wrong which Fricker describes as the set of structural and social problems that arise because ‘both speaker and hearer are labouring with the same inadequate tools’. 35 This form of injustice arises when individuals are precluded from accessing, or can only partially access, resources that could improve understanding about their experiences. Because of this asymmetry, those with unequal access to resources can suffer additional disadvantages that serve to further undermine their status and impede understanding about their condition. Kidd and Carel describe two kinds of means—which they dub ‘strategies’—by which hermeneutic injustice can be explicitly or implicitly perpetuated. 39 The first includes a range of structural barriers to participation in practices whereby knowledge is formed. Kidd and Carel argue that these can encompass physical barriers and subtler exclusions such as employing specific terminologies and conventions that serve to exclude the participation of disadvantaged people who might otherwise usefully contribute to knowledge. 39 A related, second strategy of exclusion, they argue, is the downgrading of certain forms of expression (such as first-person experiences, affective styles of presentation or vernacular) as evidence of the diminished credibility of the marginalised group. This demotion, Kidd and Carel contend, serves to further frustrate the efforts of the disadvantaged individual to participate, compounding ‘epistemic disenfranchisement’. 39 In this way, hermeneutic injustice can lead to a vicious, self-perpetuating cycle of testimonial injustice.

In what follows, we focus primarily on evidence of hermeneutic injustice, including strategies of exclusion among disabled researchers with ELC, who are largely or completely confined to the home and who seek to contribute to knowledge formation activities within the UK academy. Before we delve into the evidence, however, we offer some contextual caveats. First, it is important to offer some legal context with respect to disability rights. On the most charitable analysis, we acknowledge that not every individual who is disabled can expect to participate in every research context. For example, some barriers—such as the design or location of laboratories—might preclude full participation among some disabled researchers even with significant adaptations. Our aim then is to examine forms of epistemic injustice that pertain to ‘reasonable adjustments’, a legal term that we will unpack. Since our focus is on barriers to people with disabilities in British universities, we focus on UK legislation; however, what we have to say doubtlessly applies to other countries and regions.

Evidence of epistemic injustice among disabled researchers

Background on uk disability legislation.

Under Section 20 of the UK Equality Act 2010, higher education providers in England, Scotland and Wales are legally bound to provide ‘reasonable adjustments’ for people with disabilities who require them. 40 Section 6 of the Act defines disability as the experience of an impairment that has a ‘substantial’, long-term adverse impact on a person’s ability to engage in daily activities. Section 20 clarifies that the duty to make reasonable adjustments exists where any provisions or criteria offered or required by education providers place disabled people at a ‘substantial’ disadvantage relative to non-disabled people. 40

Health scholars have identified vagueness and therefore ambiguities in how qualifiers such as ‘substantial’ and ‘reasonable’ are interpreted. 41 Moreover, it has been contended that ‘reasonable adjustments’ rely on a non-disabled and potentially ableist perspective of what is reasonable, while also placing the burden to prove eligibility for adjustments onto disabled people, thus individualising the structural problem of normalised discrimination. 42 As previously outlined, ELC are poorly recognised as forms of disability, and research demonstrates that people living with diagnoses that can be positioned as ELC struggle to gain the recognition necessary to obtain reasonable adjustments. 32–34 43 Section 19 of the Equality Act 2010 explains that indirect discrimination occurs when one party applies a provision, criterion or practice that puts a person with a protected characteristic (such as disability) at a substantial disadvantage when compared with people without that protected characteristic. 40 44

The Equality Act allows for scenarios where discrimination may be justified (known as ‘objective justification’) in cases where providers can demonstrate that their policies or provisions constitute ‘a proportionate means of achieving a legitimate aim’. 40 Among the considerations about what might constitute a proportionate means are the size of the organisation, the practicalities and costs involved. 44 However, these are seldom explicitly articulated as a justification for the status quo, and the resulting ambiguities (which ultimately can only be resolved by tribunal or court) mean—as we will next find out—that disability discrimination may inadvertently become normalised.

Evidence of strategies of exclusion

Despite an ostensible increase in DEI policies within the academy, 45 46 there exists considerable literature demonstrating experiences of physical and attitudinal barriers to participation in academic research among disabled students and academics, including those with diagnoses that sit within the ELC umbrella. 29 31–34 43 46 There is also evidence that disability-related inequities in higher education persist in terms of degree completion, degree attainment and progression onto skilled employment or postgraduate study, within and beyond STEM. 21 22 47 48 The experience of JH is that such disparities are deeply entwined with physical and attitudinal barriers to full epistemic participation within the academy. Drawing on research findings and situating these against the lived experience of JH, we now explore evidence of strategies of exclusion for disabled researchers that, we argue, could contribute to epistemic injustice.

Studies that reveal barriers to academic participation, among people with ELC and disabled people more broadly, focus on two principal scenarios: (1) experiences of higher education students who can attend ‘on campus’ but require accommodations, 29 33 43 and (2) experiences of academics (from PhD study level upwards) navigating workplace barriers pertaining to reasonable adjustments, employment and career progression opportunities. 31 34 46 49 Where these barriers occur, we suggest they point to evidence of hermeneutical injustice that may also be underpinned by testimonial injustice. Indeed, chief among themes across such literature is that of ableism, understood as ‘a cultural imaginary and social order centred around the idealised able-bodied and -minded citizen who is self-sufficient, self-governing and autonomous’ 50 ; this ‘social order’ is founded on global prejudices about disabled bodies and minds. 50 Reports of academic ableism are evidenced as manifesting through, inter alia, a lack of accessible buildings and equipment, institutional inability or unwillingness to facilitate disability-related accommodations, and lack of familiarity (or consensus) among faculty and non-academic staff as to what constitutes disability-specific DEI practice and policy. 31 43 45 46 Additionally, increasing literature probes the creeping neoliberalisation of academia, which is contended to intersect with and perpetuate ableism, most notably though institutional normalisation of competition and hyperproductivity as a reflection of ‘excellence’. 31 46 Relatedly, and notably among students or academics with health conditions that can be positioned as ELC, the question of whether or how to disclose disability and implications of (non)disclosure is receiving critical attention. 21 29 31 33 34 43

Furthermore, as previously outlined, scarce attention has been paid to ELC explicitly, especially among people with ELC who are largely or completely confined to the home, yet may wish to continue within or enter academic spaces and thus require remote access. JH’s experience is that some of these people are not only marginalised within the academy but may be excluded from accessing it altogether. This, it would appear, is owing to a failure of institutions to facilitate remote access programmes. Here again, to understand how strategies of exclusion operate, we must turn to legal considerations. In terms of what might be considered ‘reasonable’, the willingness of research institutes to extend remote access to students and faculty during successive lockdowns owing to the SARS-CoV-2 pandemic 31 51 52 suggests that failure to extend such accommodations to disabled people who depend on them, and especially where research can be conducted from home, would be difficult to justify.

Yet, such remote access tends to be considered at best an ‘adjustment’ to preferred or ‘normal’ (non-disabled) practice, and provision appears to be patchy and poorly signposted; lack of clarity over which research institutes offer remote delivery programmes may thus constitute the initial hurdle. Some universities appear to offer remote PhDs within some disciplines but not within others, and the exclusions do not appear to be related to pragmatics such as requiring laboratory access. For example, according to JH’s enquiries, and information received, one UK research institute and member of the Russell Group (representing UK leading research-intensive institutions) offered distance learning PhD programmes in 2021 and 2022 within psychology, but not within sociology. For added context, JH’s research interests are interdisciplinary but primarily straddle disability studies (typically sited within academic schools of sociology and faculties of social sciences) and psychology. This is with a view to researching disability-affirmative, socioculturally and politically cognisant approaches to psychotherapy practice and policy. However, in academic fora, psychology and psychotherapy (often aligned with health sciences faculties) foreground heavily medicalised understandings of disability, and JH’s experience has been that psychology departments have not been open minded or welcoming vis-à-vis the prospect of integrating sociocultural and political perspectives, as per disability studies. In practice, this has meant that JH’s endeavours to find an accessible PhD have been limited to the purview of sociology. These disciplinary exclusions arguably represent the legacy of the reluctance of psychology, wider health sciences and life sciences to embrace disability in all its diversity. 21–23 50

In response to an enquiry as to why the above institution did not offer remote access PhDs in disability studies/sociology, the postgraduate admissions team informed JH: ‘All our PhD students undertake mandatory units which are only delivered in person’ (email, 10 February 2022). It is unclear how these mandatory units differ from units offered on remote access programmes. Indeed, a recurring motif throughout JH’s enquiries across various UK institutions is that further probing about potentially exclusionary policies results in ambiguous responses, or no response at all. Reasons for lack of remote access offered by other institutions included a mandatory requirement for direct (on-campus) contact with the PhD supervisor or the need to participate in onboarding sessions face to face on campus. However, lack of justification about why this was necessary was not offered.

Again, it might be expected that institutional willingness to provide remote access during lockdowns would serve as a precedent for remote access to become the norm rather than the exception. 46 However, in response to JH challenging lack of remote access provision on these grounds, the reply from the admissions team at another Russell Group university was as follows:

While during the last year some teaching and supervision has taken place online this is a temporary measure and not part of a formal distance learning course. Some supervision and teaching is also now taking place back on campus in person again. All ‘on campus’ programmes are subject to government mandated attendance requirements. (email, 28 January 2022)

When JH requested more details regarding these government-mandated attendance requirements, the admissions team declared that the enquiry would be passed onto another point of contact. Over 2 years later, no further details have been forthcoming. Ad hoc adjustments pertaining to remote delivery might be possible at some institutions, but it seems conceivable that these may be dependent on the supervisor’s individual preferences rather than policy, perhaps permitting prejudicial judgements about disability to interfere with decision-making.

Furthermore, for those fortunate enough to find a supervisor willing to ‘accommodate’ them, additional strategies of exclusion arise pertaining to funding via doctoral training programme (DTP) and research council consortiums. For example, a representative of the UK White Rose social sciences DTP 53 (covering seven UK higher education institutions in Northern England) informed JH that, in accordance with Economic and Social Research Council (ESRC) policy, disabled students confined to the home are not eligible to be considered for funding. Further digging revealed that this policy is not limited to the White Rose DTP; for example, the UK Midlands Graduate School DTP, 54 covering a further eight UK higher education institutions, lists the same exclusion criteria on its website at time of writing. When JH challenged the White Rose DTP’s policy on grounds of (dis)ableism, a representative forwarded the following response from the ESRC:

UKRI [UK Research and Innovation, non-departmental body of the UK government responsible for funding research] terms and conditions confirm that UKRI funded students must live within a reasonable travel time of their Research Organisation (RO) or collaborative organisation to ensure that they are able to maintain regular contact with their department and their supervisor. This should also ensure that the student receives the full support, mentoring, access to a broad range of training and skill development activities available at their RO, as well as access to the resources and facilities required to complete their research successfully and to a high standard. Our expectation also reflects that we want to avoid students studying in isolation […] (email, 15 December 2022)

In light of the considerable evidence that scholars across many disciplines can work remotely, the assumption that disabled people cannot research to a ‘high standard’ while confined to the home is problematic. Additionally, the reasoning around avoiding isolation, while likely well intended, does not hold much weight from JH’s standpoint. Many disabled people frequently experience significant physical and emotional isolation through navigating a (dis)ableist society and develop numerous strategies (including use of remote access technology) to mitigate this; in this respect, they may even be considered ‘experts by experience’ in resiliently striving to manage isolation. 51 55 56 Social media, for example, is used by many disabled people to connect with others, share ideas on managing health conditions and disability discrimination and develop collective advocacy and activism initiatives. 55 Refusing to offer remote access on (partial) grounds that disabled people may not be able to cope with the ensuing isolation risks infantilising people with disabilities, and withholds one of the very tools that can facilitate inclusion and thus counter isolation.

Moreover, literature suggests that being on campus does not necessarily prevent disabled people from experiencing or overcoming isolation, notably emotional isolation or alienation arising from lack of accommodations and thus feeling ‘unwelcome’ or ‘less than’. 33 46 The ESRC’s reasoning would therefore appear to arise from a non-disabled perspective (or at least, a perspective not attuned to certain facets of disability culture). Funding-related barriers are aggravated by the general lack of other funding opportunities for disabled students. For example, while scholarships for other under-represented groups are justly offered across many institutions, 57–59 often with emphasis on recruiting traditionally marginalised candidates, similar much-needed initiatives for people disadvantaged through disability are conspicuously absent. This is particularly important to address since disability and economic disadvantage are entwined in a complex manner, 2 11 and because, as previously noted, disability is intersected with other forms of social (dis)advantage. 19 21 24 28

It is worth emphasising that the exclusionary practices pertaining to health-related research, as discussed here, may be more pervasive and entrenched than we have presented. Discussing the impact of academic ableism, Brown 46 notes that disability disclosure rates, though on the increase in undergraduate admissions, drop between undergraduate and academic employment level. Brown identifies two factors that might explain this: (a) disabled academics may avoid disclosure for fear that declaring disability would impede their career, and (b) disabled students may simply drop out of the academy. As the foregoing demonstrates, JH’s experience suggests that the second factor may be entwined with disabled students being excluded from the academy because they cannot meet ‘on campus’ attendance requirements. It is currently unknown how many fledgling academics with disabilities have been excluded from the academy owing to discriminatory policies and academic culture, but it seems likely that JH’s case is not exceptional. Recent research recounts that some disabled faculty are being refused remote working arrangements as lockdown accommodations begin to revert to ‘normal’ practice. 60 For disabled researchers in perpetual lockdown, such refusals might result in experiences such as those detailed here remaining unknown and thus unaddressed.

In summary, where a ‘leaky pipeline’ exists vis-à-vis academic representation of some historically oppressed groups, 61 62 it appears that there exists no pipeline at all for a subgroup of disabled people who cannot leave their homes due to a combination of body/mind restrictions and lack of social provisions such as healthcare. Yet, disadvantages created by refusing remote access accommodations to scholars with disabilities who are confined to the home are certainly substantial. Beyond the potential loss to collective wisdom, the hermeneutical injustice perpetuated by barriers to education and employment among disabled people results in what Kidd and Carel describe as a ‘double injury’, 39 since it leads to significant ramifications for the psychosocial well-being and financial security of those excluded.

Conclusions and recommendations

Despite an ostensible increase in commitment to DEI policy and practice, the academy is far from an inclusive space for disabled people. In the case of disabled people who are unable to leave the home, we might better speak of outright exclusions as opposed to marginalisation. The above discussion has demonstrated that various strategies of exclusion operate within the academy that serve to exclude some people with disabilities ‘from the practices and places where social meanings are made and legitimated’. 39 Such exclusions risk further marginalising an already hermeneutically marginalised group, with concomitant psychosocial, occupational and financial harms. Additionally, these exclusions incur a loss of collective wisdom that adversely impacts the development of inclusive, safe and effective healthcare practice and policy.

Although we urge the importance of universities facilitating remote access to disabled scholars, we add a note of caution. First, a remote access academy should be offered in complementarity with, as opposed to an alternative to, ensuring accessibility of academic buildings and equipment, or to otherwise supporting disabled people to attend on campus. This is especially important since we also acknowledge that remote access is not a solution for all disabled people. 52 63 Of note, while remote access can be understood as an assistive technology that helps support the health, well-being and social inclusion of people with disabilities, 2 the digital divide means that disabled people are also less likely to be able to access this technology compared with their non-disabled counterparts. Such marginalisation is owing to lack of devices, broadband connectivity or reduced digital literacy, underpinned by financial, social and educational disparities as already discussed. 1 2 63 Our promotion of remote access as an inclusivity tool does not negate the need to address this divide. Nevertheless, recent research has shown that a leading UK online education provider (University of Derby) has three times as many disabled students as the national average, 30 suggesting that remote delivery of academic programmes can be a significant facilitator of DEI. We therefore conclude by offering recommendations with a view to building on such strategies of inclusion.

Given the lack of familiarity vis-à-vis disability-specific DEI practice and policy, as reported in literature 31 45 46 and as experienced by JH, our first recommendation is for formalised disability equality training and education initiatives that specifically take account of people with ELC and those confined to the home. Since report of such training reinforcing disability-related stereotyping exists, 31 there should be greater emphasis on co-producing such resources with people with disabilities, including those confined to the home who are often excluded from public policy-making. Such initiatives, which could also beneficially target personnel involved in research councils and DTPs, should address implicit personal and organisational biases, facilitate understanding of how current policy and practices perpetuate (dis)ableism and promote a proactive approach to equity and inclusion, specifically in the case of people confined to the home. Disabled researchers and disability studies scholars have argued that an institutional culture change is necessary to move beyond a perfunctory engagement in, or basic legal compliance with, DEI initiatives; a foregrounding of the social model of disability and universal design principles has thus been proposed in developing DEI policy and practice. 29 31 46 The social model upends academically prevalent (individualistic) representations of disability and reasonable adjustments, by placing the onus for change on social structures and institutions as opposed to the people who are discriminated against. 16 17 In the case of ELC, we suggest that the social structures requiring greatest change to facilitate inclusion are attitudinal contexts, most notably disbelief. 24 25 In complement to the social model, application of universal design tenets to academic contexts, which involve building ‘accommodations’ into academic standard and managing disability-related diversity proactively as opposed to reactively, 29 31 46 should be extended to remote access. In practice, this means reducing the likelihood that disabled people have to ask and prove eligibility for reasonable adjustments. 42

Second, we recommend greater institutional transparency, including clear guidance for researchers with disabilities, vis-à-vis remote working policies. For many research and study programmes, online library access, supervision and other meetings represent acceptable accommodations, if not candidates for integration into academic standard as a complement to on-campus delivery. Such accommodations should be clearly signposted and, where remote working is not possible or government mandates apply, both transparency and strong justifications are required. In this regard, an institution outside of the UK has set a precedent. Uppsala University in Sweden has welcomed JH as research affiliate in the Department of Women’s and Children’s Health, operating entirely via remote access. This approach, which embraces remote working as if it were standard practice (as per universal design principles), is invaluable in challenging the prevalent yet exclusionary academic notion of dominant (on-campus) practice and policy as ‘normal’ and ‘ability neutral’. It thus serves as an exemplar for disability-related best practice for UK institutions.

Third, the current funding system requires considerable revision to better include people with disabilities who are confined to the home. In cases where research projects can be conducted remotely, there is surely no justification for exempting this group of disabled people from being eligible to apply for grants and PhD stipends. As per our above recommendations for remote accommodations, information on funding eligibility should be easily accessible, with strong and transparent rationale for any exclusions. Additionally, existing initiatives to ring-fence funding for researchers from minoritised groups to study health-related inequities 64 should be extended to include disabled people. Without such measures, much-needed research might never be conducted. This article, which has arisen from disability standpoint, and both disability and academic allyship, has indicated a considerable research gap pertaining to how disabled students or academics confined to the home experience barriers to health-related research. With a view to addressing this research gap with the added value of disability standpoint, funding opportunities must facilitate the inclusion of disabled researchers. Yet, while some under-represented groups are supported through funding-related DEI schemes, 64 disability is often overlooked.

Finally, we recommend a more formalised and universally applied academic DEI monitoring and ombudsman scheme, both to assess DEI-related shortcomings and to support minoritised researchers in raising concerns. Disabled scholars have suggested using Disability Standard (a form of benchmarking used in business to assess inclusivity and accessibility) to analyse gaps in disability-related DEI practice and policy 31 ; practical application across UK universities appears very limited. Existing schemes to promote DEI within the education sector should ensure that disability, including disabled people confined to the home, is represented and consider how institutional compliance can be secured. ‘Advance HE’ is a UK non-governmental body that promotes excellence in higher education, an objective the body acknowledges as entwined with DEI. 65 While DEI ‘international charters’ pertaining to gender and race exist with a view to encouraging providers to commit to inclusion of under-represented groups, 65 an equivalent charter specifically for disability does not exist. Here again, we recognise that different forms of discrimination intersect and that race and gender shape disability. 2 21 28 Moreover, while we do not mean to overlook recent efforts among Advance HE and other bodies to include disability in DEI initiatives, 66 the voluntary nature of many of these initiatives (which ‘encourage’ higher education institutions to address more fully disability-related DEI) will likely allow the inequitable status quo to persist. Seeking to ground a collective institutional commitment to disability inclusion within legislation, or at the very least within a transparent ‘award’ system as with DEI initiatives pertaining to other under-represented groups, 65 would likely lend more gravitas to such schemes and ‘nudge’ research institutes towards greater accountability.

In summary, insights from scholars with disabilities can help to inform more inclusive, safe and effective health-related interventions, with further benefits for social inclusion. Current academic structures deny opportunities to the very people who are well placed to identify and research the most overlooked problems in our health systems. If we truly prize DEI, the academy must become more accessible to disabled people.

Ethics statements

Patient consent for publication.

Not applicable.

• Walker DK ,
• Correa-De-Araujo R
• Equality and Human Rights Commission
• Rotarou ES ,
• Sakellariou D
• Sakellariou D ,
• Gaze S , et al
• Iezzoni LI ,
• Ressalam J , et al
• Office for National Statistics
• US Bureau of Labor Statistics
• Lillywhite A ,
• Shakespeare T
• Campbell FK
• Egambaram O ,
• Leigh J , et al
• The Royal society
• APPG on diversity and inclusion in STEM and British Science Association
• Benstead S ,
• Cockerill V ,
• Green P , et al
• Hamilton PR ,
• Harrison ED
• Rexhepi H , et al
• ↵ Equality act . 2010 . Available : https://www.legislation.gov.uk/ukpga/2010/15/contents
• Shrewsbury D
• Wolbring G ,
• Lillywhite A
• Stone S-D ,
• Crooks VA ,
• White Rose Social Sciences DTP
• ↵ Midlands graduate school ESRC DTP . Eligibility requirements , 2023 . Available : https://warwick.ac.uk/fac/cross_fac/mgsdtp/studentships/eligibility/
• Moseley RL ,
• Wignall L , et al
• University of Leeds
• Open University
• University of Oxford
• Nicholson J ,
• Campbell FK , et al
• Pettersson L ,
• Johansson S ,
• Demmelmaier I , et al

X @JoElizaHunt, @crblease

JH and CB contributed equally.

Contributors Both authors contributed equally to all aspects of the paper. As corresponding author, JH acts as guarantor.

Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests None declared.

Provenance and peer review Not commissioned; externally peer reviewed.

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Josh Gibson becomes MLB career and season batting leader as Negro Leagues statistics incorporated

FILE - Baseball catcher Josh Gibson in an undated photo. Josh Gibson became Major League Baseball’s career leader with a .372 batting average, surpassing Ty Cobb’s .367, when records of the Negro Leagues for more than 2,300 players were incorporated after a three-year research project. (AP Photo/File)

The grave stone for baseball player Josh Gibson is shown at Allegheny Cemetery in Pittsburgh on March 17, 2017. Gibson became Major League Baseball’s career batting leader with a .372 average, surpassing Ty Cobb’s .367 when records of the Negro Leagues for more than 2,300 players were incorporated Tuesday, May 28, 2024, after a three-year research project. (AP Photo/Keith Srakocic, File)

FILE - Sean Gibson, the executive director of the Josh Gibson Foundation, poses next to a poster at the Pittsburgh Opera House in Pittsburgh for the upcoming opera about his great-grandfather, baseball player Josh Gibson, on March 17, 2017. Josh Gibson became Major League Baseball’s career batting leader with a .372 average, surpassing Ty Cobb’s .367 when records of the Negro Leagues for more than 2,300 players were incorporated Tuesday, May 28, 2024, after a three-year research project. (AP Photo/Keith Srakocic, File)

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NEW YORK (AP) — Josh Gibson became Major League Baseball’s career leader with a .372 batting average, surpassing Ty Cobb’s .367, when Negro Leagues records for more than 2,300 players were incorporated Tuesday after a three-year research project.

Gibson’s .466 average for the 1943 Homestead Grays became the season standard, followed by Charlie “Chino” Smith’s .451 for the 1929 New York Lincoln Giants. They overtook the .440 by Hugh Duffy for the National League’s Boston team in 1894.

Gibson also became the career leader in slugging percentage (.718) and OPS (1.177), moving ahead of Babe Ruth (.690 and 1.164).

“It’s a show of respect for great players who performed in the Negro Leagues due to circumstances beyond their control and once those circumstances changed demonstrated that they were truly major leaguers,” baseball Commissioner Rob Manfred said Wednesday in an interview with The Associated Press. “Maybe the single biggest factor was the success of players who played in the Negro Leagues and then came to the big leagues.”

AP AUDIO: Josh Gibson becomes MLB career and season batting leader as Negro Leagues statistics incorporated

AP Washington correspondent Sagar Meghani reports Major League Baseball’s record book looks a lot different, with Negro League records now incorporated.

A special committee on baseball records decided in 1969 to recognize six major leagues dating to 1876: the National (which launched in 1876), the American (1901), the American Association (1882-1891), Union Association (1884), Players’ League (1890) and Federal League (1914-1915). It excluded the National Association (1871-75), citing an “erratic schedule and procedures.”

MLB announced in December 2020 that it would be “correcting a longtime oversight” and would add the Negro Leagues . John Thorn, MLB’s official historian, chaired a 17-person committee that included Negro Leagues experts and statisticians.

“The condensed 60-game season for the 2020 calendar year for the National League and American League prompted us to think that maybe the shortened Negro League seasons could come under the MLB umbrella, after all,” Thorn said.

An updated version of MLB’s database will become public before the St. Louis Cardinals and San Francisco Giants play a tribute game to the Negro Leagues on June 20 at Rickwood Field in Birmingham, Alabama.

Baseball Hall of Fame President Josh Rawitch said statistics on Cooperstown plaques will remain the same because they reflect the information available at the time of a player’s induction.

Standards for season leaders is the same for Negro Leagues as the other leagues: 3.1 plate appearances or one inning for each game played by a player’s team.

Gibson’s .974 slugging percentage in 1937 becomes the season record, and Barry Bonds’ .863 in 2001 dropped to fifth, also trailing Mules Suttles’ .877 in 1926, Gibson’s .871 in 1943 and Smith’s .870 in 1929.

Bond’s prior OPS record of 1.421 in 2004 dropped to third behind Gibson’s 1.474 in 1937 and 1.435 in 1943.

Willie Mays gained 10 hits from the 1948 Birmingham Black Barons, increasing his total to 3,293. Minnie Minoso surpassed 2,000 hits, credited with 150 for the New York Cubans from 1946-1948 that boosted his total to 2,113.

Jackie Robinson, who broke MLB’s color barrier with the 1947 Dodgers, was credited with 49 hits with the 1945 Kansas City Monarchs that increased his total to 1,567.

Among pitchers, Satchel Paige gained 28 wins that raised his total to 125.

The committee met six times and dealt with issues such as when compiled league statistics didn’t make sense, such as a league having more wins than losses and walks that were missing. Researchers had to identify whether players with the same name were one person or separate, tracking dates of birth, and identify people listed by nicknames. Documenting transactions and identifying ballparks in a time when neutral sites often were used is ongoing, along with uncovering statistics for independent teams.

“We made the decision at a point in time that we became convinced that it was possible to get accurate statistics that could be appropriately integrated into our record books,” Manfred said.

Kevin Johnson and Gary Ashwill, researchers who had spent nearly two decades helping assemble the Seamheads Negro Leagues Database, were included in the project.

Thorn estimated 72% of Negro Leagues records from 1920-1948 are included and additional research might lead to future modifications. Thorn said a four-homer game by Gibson in 1938 and a home run by Mays in August 1948 could not be included because complete game accounts have not been found.

“Without a box score, we can’t really balance the statistics,” Johnson said. “Those games are kind of in limbo at the moment.”

Records include the first Negro National League (1920-31), Eastern Colored League (1923-28), American Negro League (1929), East-West League (1932), Negro Southern League (1932), second Negro National League (1933-48) and Negro American League (1937-48). Barnstorming exhibition games are not included.

Some game details were obtained from newspapers that covered the Black communities. Johnson said while complete accounts were found for about 95% of games in the 1920s, coverage dropped off during the Great Depression in the 1930s and never fully recovered.

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