Tackling challenges of global health electives: Resident experiences of a structured and supervised medicine elective within an existing global health partnership
Bibliographic record
Abstract
BACKGROUND: The Toronto-Addis Ababa Academic Collaboration in Emergency Medicine (TAAAC-EM) deploys teaching teams of Canadian EM faculty to Addis Ababa to deliver a longitudinal residency curriculum. Canadian trainees participate in these teams as a formally structured and supervised elective in global health (GH) and EM, which has been designed to enhance the strength of GH electives and address key challenges highlighted in the literature. METHODS: The purpose of this qualitative study was to identify, describe, and evaluate strengths and weaknesses of this elective in relation to its purposeful structure. Residents who completed the elective were invited to participate in face-to-face interviews to discuss their experiences. RESULTS: The findings show that the residents both chose this elective because of its purposefully designed features, and that these same features increased their enjoyment and the educational benefit of the elective. Supervised bedside teaching, relationships shared with Ethiopian residents, and the positive impact the experience had on their clinical practice in Canada were identified as the primary strengths. CONCLUSION: Purposeful and thoughtful design of global health electives can enhance the resident learning experience and mitigate challenges for trainees seeking global health training opportunities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".