Core components of an anti-racist approach among health professions educators: an integrative review
Bibliographic record
Abstract
Objectives: This integrative literature review aimed to identify the core elements of an anti-racist approach among health professions educators. Methods: We searched five databases CINAHL (EBSCOhost), ERIC (ProQuest Dissertations & Thesis Global), EMBASE (Ovid), MEDLINE (Ovid), and Web of Science (Social Sciences Citation Index, Citation Index Expanded) in March 2021. The search strategy combined concepts related to anti-racist pedagogies in the context of health professions education by educators in any capacity. From 1,755 results, we selected 249 manuscripts published in English or French between 2008 and 2021 based on titles and abstracts. After reviewing the full texts, we selected the 48 most relevant sources. We extracted data regarding knowledge, skills, and attitudes in reference to anti-racist approaches or surrogate terms. Within each category, we grouped similar data using a conceptual map. Results: Analysis of the selected sources revealed that, for health professions educators, engaging in an anti-racist pedagogical approach requires more than incorporating racialized perspectives and content into the classroom. It rather rests on three interrelated components: developing a critical understanding of power relationships, moving toward a critical consciousness, and taking action at individual and organizational levels. Conclusions: This review sheds light on knowledge, attitudes and skills that educators must deploy to adopt an anti-racist approach competently. This approach is a learned, intentional, and strategic effort in which health professions educators incorporate anti-racism into their teaching and apply anti-racist values to their various spheres of influence. This ongoing process strives for institutional and structural changes and requires whole-system actions.
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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 | 0.010 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.020 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".