Transforming our relationship with the social determinants of health: a scoping review of social justice interventions in Canadian Medical Schools
Bibliographic record
Abstract
This article was migrated. The article was marked as recommended. Background/Purpose: Physicians are in a powerful position to improve the health status of communities through mitigating disparities rooted in social inequities. However, it is uncertain whether medical schools are preparing future physicians with the skills needed to care for diverse populations. The current scoping review aimed to describe how Canadian medical schools teach social justice, comparing pedagogical strategies. Methods: A search was performed using OVID to identify published studies of implemented and evaluated social justice-based interventions within Canadian medical school curricula. Results: Six studies were included. Common themes included increased content knowledge, greater understanding of SDoH, acknowledgement of power and privilege imbalances, identification of physicians' roles as advocates, emphasis on the importance of interdisciplinary care, and increased capacity for self-reflection and personal growth. Experiential interventions were associated with greater personal transformation, but had limited accessibility. Conclusion: Despite the widespread recognition of physicians' roles as health advocates, there is a lack of consensus about an effective strategy for teaching social justice in medical education in Canada. While additional research focusing on the relative merits of didactic versus experiential learning is needed, these preliminary results suggest that experiential learning emphasizing self-reflection and personal growth may be optimal when approaching transformative learning.
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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 | 0.016 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".