Educational Effects of International Health Electives on U.S. and Canadian Medical Students and Residents
Bibliographic record
Abstract
PURPOSE: To evaluate the educational effects of international health electives (IHEs) on participants. IHEs are a popular component of many medical school and residency program curricula, and are reported to provide benefits in knowledge, skills, and attitudes. METHOD: The authors reviewed all studies reported in Medline and ERIC databases that have assessed the educational effects of IHEs on U.S. and Canadian medical students and residents. Data extracted from eligible studies included type and duration of IHE, details of survey instrument, response rate, comparison group, and outcomes. Seven of the eight eligible studies assessed educational effects on participants using self-reported questionnaires; a single study used an objective measurement of knowledge. RESULTS: Eight studies involving 522 medical students and 166 residents met inclusion criteria. IHEs appear to be associated with career choices in underserved or primary care settings and recruitment to residency programs. They also appear to have positive effects on participants' clinical skills, certain attitudes, and knowledge of tropical medicine. CONCLUSION: IHEs appear to have positive educational influences on participants' knowledge, skills, and attitudes. Furthermore, IHEs may play some role both in recruiting residents and in their choices of careers in primary care and underserved settings. Future directions for research in this field are discussed.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".