Teaching the Social Determinants of Health: A Path to Equity or a Road to Nowhere?
Bibliographic record
Abstract
Medical schools are increasingly called to include social responsibility in their mandates. As such, they are focusing their attention on the social determinants of health (SDOH) as key drivers in the health of the patients and communities they serve. However, underlying this emphasis on the SDOH is the assumption that teaching medical students about the SDOH will lead future physicians to take action to help achieve health equity. There is little evidence to support this belief. In many ways, the current approach to the SDOH within medical education positions them as "facts to be known" rather than as "conditions to be challenged and changed." Educators talk about poverty but not oppression, race but not racism, sex but not sexism, and homosexuality but not homophobia. The current approach to the SDOH may constrain or even incapacitate the ability of medical education to achieve the very goals it lauds, and in fact perpetuate inequity. In this article, the authors explore how "critical consciousness" and a recentering of the SDOH around justice and inequity can be used to deepen collective understanding of power, privilege, and the inequities embedded in social relationships in order to foster an active commitment to social justice among medical trainees. Rather than calling for minor curricular modifications, the authors argue that major structural and cultural transformations within medical education need to occur to make educational institutions truly socially responsible.
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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 | 0.020 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.053 |
| Scholarly communication | 0.013 | 0.021 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.006 | 0.015 |
| Insufficient payload (model declined to judge) | 0.009 | 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".