Intersectionality in Medical Education: A Meta-Narrative Review
Bibliographic record
Abstract
Introduction: Despite increasing attention to improving equity, diversity, and inclusion in academic medicine, a theoretically informed perspective to advancing equity is often missing. Intersectionality is a theoretical framework that refers to the study of the dynamic nature of social categories with which an individual identifies and their unique localization within power structures. Intersectionality can be a useful lens to understand and address inequity, however, there is limited literature on intersectionality in the context of medical education. Thus, we explored how intersectionality has been conceptualized and applied in medical education. Methods: We employed a meta-narrative review, analyzing existing literature on intersectionality theory and frameworks in medical education. Three electronic databases were searched using key terms yielding 32 articles. After, title, abstract and full-text screening 14articles were included. Analysis of articles sought a meaningful synthesis on application of intersectionality theory to medical education. Results: Existing literature on intersectionality discussesthe role of identity categorization and the relationship between identity, power, and social change. There are contrasting narratives on the practical application of intersectionality to medical education, producing tensions between how intersectionality is understood as theory and how it is translated in practice. Discussion: A paucity in literature on intersectionality in medical education suggests that there is a risk intersectionality may be understood in a superficial manner and considered a synonym for diversity. Drawing explicit attention to its core tenets of reflexivity, transformational identity, and analysis of power is important to maintain fidelity to how intersectionality is understood in broader critical social science literature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.016 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".