Twelve tips for undertaking reflexive global health experiences in medicine
Bibliographic record
Abstract
BACKGROUND: While interest and opportunities for global health experiences (GHE) continue to grow, the preparation of students and health professionals alike to engage in these GHEs remains limited. AIMS: This article provides tips for reflexivity prior to undertaking a GHE and suggests ways to debrief the experience in order to ensure that trainees and professionals that engage in GHEs can both help their intended communities and also get the most out of the experience. METHODS: The authors conducted a scoping review using Medline, PubMed and Google scholar using searching the terms: global health, global health experience, global health research, and international medical elective. We supplemented this search with our own experiences working with international partners. CONCLUSIONS: GHEs should be undertaken with reflexivity prior to, during and subsequent to the experience in order to ensure that all collaborators in the partnership meet their intended goals.
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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 | 0.097 | 0.164 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.012 | 0.029 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.011 | 0.017 |
| Insufficient payload (model declined to judge) | 0.007 | 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".