Expert Consensus on Inclusion of the Social Determinants of Health in Undergraduate Medical Education Curricula
Bibliographic record
Abstract
PURPOSE: Accreditation bodies have mandated teaching social determinants of health (SDH) to medical students, but there has been limited guidance for educators on what or how to teach, and how to evaluate students' competence. To fill this gap, this study aimed to develop an SDH curricular consensus guide for teaching SDH to medical students. METHOD: In 2017, the authors used a modified Delphi technique to survey an expert panel of educators, researchers, students, and community advocates about knowledge, skills, and attitudes (KSA) and logistics regarding SDH teaching and assessment. They identified the panel and ranked a comprehensive list of topics based on a scoping review of SDH education studies and discussions with key informants. A total of 57 experts were invited. RESULTS: Twenty-two and 12 panelists participated in Delphi rounds 1 and 2, respectively. The highest-ranked items regarding KSA were "Appreciation that the SDH are some of the root causes of health outcomes and health inequities" and "How to work effectively with community health workers." The panel achieved consensus that SDH should constitute 29% of the total curriculum and be taught continuously throughout the curriculum. Multiple-choice tests were ranked lowest as an assessment method, and patient feedback was ranked highest. Panelists noted that SDH content must be a part of standardized exams to be prioritized by faculty and students. CONCLUSIONS: An expert panel endorsed essential curricular content, teaching methods, and evaluation approaches that can be used to help guide medical educators regarding SDH curriculum development.
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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 | 0.357 | 0.369 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.007 | 0.015 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".