Aligning family medicine residency training with societal needs: An international Delphi study
Bibliographic record
Abstract
INTRODUCTION: Primary care is evolving around the world, and the need to address changing societal needs prompted a call to renew family medicine residency training. Experts were asked to generate learning topics around six areas of societal need. METHODS: International family medicine experts were invited to participate in a Delphi study that concluded in 2024. The initial round generated 1275 submissions. Through content analysis, we distilled these submissions to 54 learning topics, which were rated for their importance using a 5-point scale. Importance was indicated if the median score was ≥3.5, and consensus was achieved if the interquartile range was ≤1. RESULTS: All learning topics were deemed important. Consensus was reached for 46 of 54 learning topics. Topics with low consensus included 'community support and engagement,' 'holistic understanding and proficiency in trauma-informed care,' 'historical and cultural aspects of Indigenous communities,' and 'acquiring research and scholarship competencies in virtual healthcare.' CONCLUSIONS: Consensus was reached in 46 learning topics, with low consensus in areas where physicians' roles overlap with other care providers. Learning topics generated in this study could be integrated to ensure residency programs meet current societal needs. These findings are relevant to family medicine and many residency programs.
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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 | 0.063 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".