Engaging intersectionality in medical education
Bibliographic record
Abstract
Medical education (ME) plays a critical role in shaping future healthcare providers; however, systemic inequities persist due to biases embedded in both the formal and hidden curricula. The hidden curriculum-unspoken values, norms, and structural inequalities-reinforces implicit biases that influence professional identity formation, clinical decision-making, and patient outcomes. This theoretical paper examines how overlapping social identities can shape health experiences and access to care, and establishes a foundation for tackling systemic inequalities by advocating for the integration of an intersectional framework into ME. As healthcare institutions increasingly focus on diversity and inclusion, we aim to demonstrate that integrating intersectionality theory into ME is a timely and necessary step towards training physicians to meet the needs of diverse patient populations and reduce care disparities. We highlight how the absence of intersectional perspectives in medical training results in narrow clinical frameworks, reduced cultural competency, and the perpetuation of health disparities through the hidden curriculum. Furthermore, we outline practical strategies for embedding intersectionality into ME, such as building an intersectional curriculum, incorporating diverse case scenarios, and establishing institutional task forces. Despite potential challenges, such as resistance to change and resource constraints, implementing intersectionality in ME remains essential and can be attainable through institutional commitment and collaborative approaches. By using intersectionality as a guiding framework, ME can better prepare future healthcare providers to deliver equitable patient centered care while reducing the systemic disparities in healthcare.
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 | 0.046 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.019 | 0.037 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.003 | 0.065 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.010 | 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".