Abstract
PurposeThere is growing concern that inequities in methods of selection into medical specialties reduce specialist cohort diversity, particularly where measures designed for another purpose are adapted for specialist selection, prioritising reliability over validity. This review examined how empirical measures affect the diversity of specialist selection. The goals were to summarise the groups for which evidence is available, evaluate evidence that measures prioritising reliability over validity contribute to under-representation, and identify novel measures or processes that address under-representation, in order to make recommendations on selection into medical specialties and research required to support diversity.MethodIn 2020–1, the authors implemented a comprehensive search strategy across 4 electronic databases (Medline, PsychINFO, Scopus, ERIC) covering years 2000–2020, supplemented with hand-search of key journals and reference lists from identified studies. Articles were screened using explicit inclusion and exclusion criteria designed to focus on empirical measures used in medical specialty selection decisions.ResultsThirty-five articles were included from 1344 retrieved from databases and hand-searches. In order of prevalence these papers addressed the under-representation of women (21/35), international medical graduates (10/35), and race/ethnicity (9/35). Apart from well-powered studies of selection into general practice training in the UK, the literature was exploratory, retrospective, and relied upon convenience samples with limited follow-up. There was preliminary evidence that bias in the measures used for selection into training might contribute to under-representation of some groups.ConclusionsThe review did not find convincing evidence that measures prioritising reliability drive under-representation of some groups in medical specialties, although this may be due to limited power analyses. In addition, the review did not identify novel specialist selection methods likely to improve diversity. Nevertheless, significant and divergent efforts are being made to promote the evolution of selection processes that draw on all the diverse qualities required for specialist practice serving diverse populations. More rigorous prospective research across different national frameworks will be needed to clarify whether eliminating or reducing the weighting of reliable pre-selection academic results in selection decisions will increase or decrease diversity, and whether drawing on a broader range of assessments can achieve both reliable and socially desirable outcomes.
Highlights
There is long-standing recognition that medical workforces do not represent the diversity of the populations they serve [1]
In order of prevalence these papers addressed the under-representation of women (21/35), international medical graduates (10/35), and race/ ethnicity (9/35)
To quantify the potential bias of having a greater probability of identifying articles from US/UK than elsewhere we identified the articles which were included in our review which were not identified by our basic search, but which were added as a result of the specific search terms above
Summary
There is long-standing recognition that medical workforces do not represent the diversity of the populations they serve [1]. One report noted that African Americans, Hispanic Americans, and American Indians comprised more than a quarter of the US population but only 6% of its physicians [1]. The same report argued that increased diversity of the health workforce was justified both to support social justice, and as an effective means of improving population health by improving cultural competence, communication, patient trust, and reducing barriers to care [1, 6]. In response to similar concerns, some medical schools have developed socially accountable education frameworks where community collaboration, equitable selection criteria not solely focused on academic performance, and learning experiences in areas of need are used to encourage recruitment and retention to rural and other underserved populations [7]
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