Integrating the social determinants of health into graduate medical education training: a scoping review
Bibliographic record
Abstract
Background The social determinants of health (SDH) play a key role in the health of individuals, communities, and populations. Academic institutions and clinical licensing bodies increasingly recognize the need for healthcare professionals to understand the importance of considering the SDH to engage with patients and manage their care effectively. However, incorporating relevant skills, knowledge, and attitudes relating to the SDH into curricula must be more consistent. This scoping review explores the integration of the SDH into graduate medical education training programs. Methods A systematic search was performed of PubMed, Ovid MEDLINE, ERIC, and Scopus databases for articles published between January 2010 and March 2023. A scoping review methodology was employed, and articles related to training in medical or surgical specialities for registrars and residents were included. Pilot programs, non-SDH-related programs, and studies published in languages other than English were excluded. Results The initial search produced 829 articles after removing duplicates. The total number of articles included in the review was 24. Most articles were from developed countries such as the USA (22), one from Canada, and only one from a low- and middle-income country, Kenya. The most highly represented discipline was pediatrics. Five papers explored the inclusion of SDH in internal medicine training, with the remaining articles covering family medicine, obstetrics, gynecology, or a combination of disciplines. Longitudinal programs are the most effective and frequently employed educational method regarding SDH in graduate training. Most programs utilize combined teaching methods and rely on participant surveys to evaluate their curriculum. Conclusion Applying standardized educational and evaluation strategies for SDH training programs can pose a challenge due to the diversity of the techniques reported in the literature. Exploring the most effective educational strategy in delivering these concepts and evaluating the downstream impacts on patient care, particularly in surgical and non-clinical specialities and low- and middle-income countries, can be essential in integrating and creating a sustainable healthcare force
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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 | 0.018 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.022 | 0.021 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 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".