The ethics and safety of medical student global health electives
Bibliographic record
Abstract
OBJECTIVE: To explore and characterize the ethical and safety challenges of global health experiences as they affect medical students in order to better prepare trainees to face them. METHODS: Semi-structured interviews were conducted with 23 Canadian medical trainees who had participated in global health experiences during medical school. Convenience and snowball sampling were utilized. Using Moustakas's transcendental phenomenological approach, participant descriptions of ethical dilemmas and patient/trainee safety problems were analyzed. This generated an aggregate that illustrates the essential meanings of global health experience ethical and safety issues faced. RESULTS: We interviewed 23 participants who had completed 38 electives (71%, n=27, during pre-clinical years) spending a mean 6.9 weeks abroad, and having visited 23 countries. Sixty percent (n=23) had pre-departure training while 36% (n=14) had post-experience debriefing. Three macro-level themes were identified: resource disparities and provision of care; navigating clinical ethical dilemmas; and threats to trainee safety. CONCLUSIONS: Medical schools have a responsibility to ensure ethical and safe global health experiences. However, our findings suggest that medical students are often poorly prepared for the ethical and safety dilemmas they encounter during these electives. Medical students require intensive pre-departure training that will prepare them emotionally to deal with these dilemmas. Such training should include discussions of how to comply with clinical limitations.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".