Enhanced skills in global health and health equity: Guidelines for curriculum development
Bibliographic record
Abstract
INTRODUCTION: Global health addresses health inequities in the care of underserved populations, both domestic and international. Given that health systems with a strong primary care foundation are the most equitable, effective and efficient, family medicine is uniquely positioned to engage in global health. However, there are no nationally recognized standards in Canada for postgraduate family medicine training in global health. OBJECTIVE: To generate consensus on the essential components of a Global Health/Health Equity Enhanced Skills Program in family medicine. METHODS: A panel comprised of 34 experts in global health education and practice completed three rounds of a Delphi small group process. RESULTS: Consensus (defined as ≥ 75% agreement) was achieved on program length (12 months), inclusion of both domestic and international components, importance of mentorship, methods of learner assessment (in-training evaluation report, portfolio), four program objectives (advocacy, sustainability, social justice, and an inclusive view of global health), importance of core content, and six specific core topics (social determinants of health, principles and ethics of health equity/global health, cultural humility and competency, pre and post-departure training, health systems, policy, and advocacy for change, and community engagement). CONCLUSION: Panellists agreed on a number of program components forming the initial foundation for an evidence-informed, competency-based Global Health/Health Equity Enhanced Skills Program in family medicine.
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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 | 0.074 | 0.123 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.013 | 0.009 |
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".