The need for critical and intersectional approaches to equity efforts in postgraduate medical education: A critical narrative review
Bibliographic record
Abstract
BACKGROUND: Racialised trainees in Canada and the USA continue to disproportionately experience discrimination and harassment in learning environments despite equity, diversity, and inclusion (EDI) reform efforts. Using critical approaches to understand what problems have been conceptualised and operationalised as EDI issues within postgraduate medical education (PGME) is important to inform ongoing learning environment reform in resident training. METHODS: We conducted a critical narrative review of EDI literature from 2009-2022 using critical race theory (CRT) and the concept of intersectionality to analyse how issues of discrimination in PGME have been studied. Our search yielded 2244 articles that were narrowed down to 349 articles for relevance to Canadian and American PGME contexts. We attended to reflexivity and our positionality in analysing the database and identifying themes related to EDI reform. RESULTS: Interest convergence was noted in how EDI reform was rationalised primarily by increased productivity. Problems of learner representation, gender inequities and curricular problems were conceptualised as EDI issues. The role that racism played in EDI-related problems was largely invisible, as were explicit conceptualisations of race and gender as social constructs. Overall, there was a lack of critical or intersectional approaches in the literature reviewed. Misalignment was noted where studies would frame a problem through a critical lens, but then study the problem without attention to power. DISCUSSION: Interest convergence and epistemic injustice can account for the absence of critical approaches due to the alignment of existing EDI work with institutional interests and priorities. Interest convergence conceptually limits existing EDI reform efforts in PGME. CRT and intersectionality connect racialised learner experiences to systemic phenomena like racism and other forms of discrimination to challenge dominant assumptions. Because they attend to power, critical approaches are key to understanding why inequities have persisted to advance equity in learning environments for racialised and intersectionally marginalised learners.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.076 | 0.217 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.014 | 0.021 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".