Global health competencies in postgraduate medical education: a scoping review and mapping to the CanMEDS physician competency framework
Bibliographic record
Abstract
Background: Global Health opportunities are popular, with many reported benefits. There is a need however, to identify and situate Global Health competencies within postgraduate medical education. We sought to identify and map Global Health competencies to the CanMEDS framework to assess the degree of equivalency and uniqueness between them. Methods: JBI scoping review methodology was utilized to identify relevant papers searching MEDLINE, Embase, and Web of Science. Studies were reviewed independently by two of three researchers according to pre-determined eligibility criteria. Included studies identified competencies in Global Health training at the postgraduate medicine level, which were then mapped to the CanMEDS framework. Results: A total of 19 articles met criteria for inclusion (17 from literature search and two from manual reference review). We identified 36 Global Health competencies; the majority (23) aligned with CanMEDS competencies within the framework. Ten were mapped to CanMEDS roles but lacked specific key or enabling competencies, while three did not fit within the specific CanMEDS roles. Conclusions: We mapped the identified Global Health competencies, finding broad coverage of required CanMEDS competencies. We identified additional competencies for CanMEDS committee consideration and discuss the benefits of their inclusion in future physician competency frameworks.
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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 | 0.021 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.024 | 0.025 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".