Developing consensus for postgraduate global health electives: definitions, pre-departure training and post-return debriefing
Bibliographic record
Abstract
BACKGROUND: Global health (GH) electives are on the rise, but with little consensus on the need or content of pre-departure training (PDT) or post-return debriefing (PRD) for electives in postgraduate medical education. METHODS: Using a 2-iteration Delphi process to encourage discussion and consensus, participants from 14 medical schools across Canada provided input to promote more uniform policy towards defining GH electives, when PDT and PRD should be mandatory and what curriculum should be included. RESULTS: There is consensus that PDT and PRD should be mandatory for international electives. Respondents felt that PDT should include a broad range of topics including objectives, travel safety, personal health, logistics, ethics of GH, scope of practice/supervision, and cultural awareness. PRD should include elective evaluation, lessons learned, knowledge translation, review of health and safety, and issues of reintegration. The format of PDT and PRD needs to be individualized to each institution to fit within the limitations of faculty who can serve as facilitators. Global health educators agreed on the importance of mandatory PDT and PRD for remote Canadian aboriginal electives, but did not feel that they could make recommendations without additional input of aboriginal scholars. CONCLUSIONS: All residency programs that send residents on international electives should work towards instituting quality, mandatory PDT and PRD. PDT and PRD should be recognized by universities as having academic merit and by program directors as core resident learning activities. Curriculum and objectives could be arranged around CanMEDS competencies, a physician competency framework that emphasizes qualities beyond medical expert such as professionalism, health advocate, and collaborator.
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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 | 0.244 | 0.290 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".