Strengthening Equity, Diversity, and Inclusion in medical education via an intersectional approach
Bibliographic record
Abstract
Abstract Background In the wake of COVID-19, equity, diversity, and inclusion (EDI) policies have been taken up in the health field, yet the actual impact of these initiatives, particularly for multiply marginalized individuals, is debated. Despite progress in areas such as diversity promotion, EDI has been critiqued as superficial and as failing to challenge the drivers of health inequity. This presentation discusses how an intersectionality-informed approach can help harness the transformative potential of EDI. Methods This research is informed by a literature review of EDI initiatives in the health field, and is an offshoot of a 4.5 year international project funded by the Canadian Institutes for Health Research focused on evaluating and strengthening intersectionality-informed policy guidance. Results There is a disconnect between EDI commitments and substantive action on health inequities and an intersectional approach can help bridge this divide. We demonstrate this with a case example which brings an intersectional approach to EDI initiatives in medical education - including bias trainings, courses on the social determinants of health, and the engagement of community groups. Drawing on promising equity promoting work across these areas, we show how intersectionality can harness the potential of EDI by bringing to the fore what has often been missing, including the promotion of health stakeholder reflexivity, attention to interacting systems and structures shaping health, and meaningful engagement with underrepresented communities. Conclusions An intersectionality-informed approach to EDI has the potential to enhance the health equity impacts of initiatives within medical education and the broader health field. Key messages • EDI initiatives in health often have limited impact on health inequities. • An intersectionality-informed approach to EDI can help health stakeholders drive transformative action on health inequities.
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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 | 0.088 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.023 | 0.083 |
| Scholarly communication | 0.032 | 0.023 |
| Open science | 0.005 | 0.087 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.013 | 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".