Support infrastructure available to Canadian residents completing post-graduate global health electives: current state and future directions
Bibliographic record
Abstract
BACKGROUND: Global health electives offer medical trainees the opportunity to broaden their clinical horizons. Canadian universities have been encouraged by regulatory bodies to offer institutional support to medical students going abroad; however, the extent to which such support is available to residents has not been extensively studied. METHODS: We conducted a survey study of Canadian universities examining the institutional support available to post-graduate medical trainees before, during, and after global health electives. RESULTS: Responses were received from 8 of 17 (47%) Canadian institutions. Results show that trainees are being sent to diverse locations around the world with more support than recommended by post-graduate regulatory bodies. However, we found that the content of the support infrastructure varies amongst universities and that certain components-pre-departure training, best practices, risk management, and post-return debriefing-could be more thoroughly addressed. CONCLUSION: Canadian universities are encouraged to continue to send their trainees on global health electives. To address the gaps in infrastructure reported in this study, the authors suggest the development of comprehensive standardized guidelines by post-graduate regulatory/advocacy bodies to better ensure patient and participant safety. We also encourage the centralization of infrastructure management to the universities' global health departments to aid in resource management.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".