Systematic review of specialist selection methods with implications for diversity in the medical workforce

Amos, Andrew James, Lee, Kyungmi, Sen Gupta, Tarun, and Malau-Aduli, Bunmi S. (2021) Systematic review of specialist selection methods with implications for diversity in the medical workforce. BMC Medical Education, 21. 448.

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Abstract

Purpose: There 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.

Method: In 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.

Results: Thirty-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.

Conclusions: The 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.

Item ID: 69411
Item Type: Article (Research - C1)
ISSN: 1472-6920
Copyright Information: © The Author(s). 2021Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License,which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you giveappropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate ifchanges were made. The images or other third party material in this article are included in the article's Creative Commonslicence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commonslicence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtainpermission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to thedata made available in this article, unless otherwise stated in a credit line to the data.
Date Deposited: 21 Sep 2021 21:40
FoR Codes: 32 BIOMEDICAL AND CLINICAL SCIENCES > 3299 Other biomedical and clinical sciences > 329999 Other biomedical and clinical sciences not elsewhere classified @ 50%
39 EDUCATION > 3901 Curriculum and pedagogy > 390110 Medicine, nursing and health curriculum and pedagogy @ 50%
SEO Codes: 16 EDUCATION AND TRAINING > 1603 Teaching and curriculum > 160301 Assessment, development and evaluation of curriculum @ 50%
20 HEALTH > 2099 Other health > 209999 Other health not elsewhere classified @ 50%
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