Ethical issues encountered by medical students during international health electives
Bibliographic record
Abstract
CONTEXT: Medical students increasingly wish to participate in international health electives (IHEs). The authors undertook to understand from the students' perspective the ethical challenges encountered on IHEs in low-resource settings and how students respond to these issues. METHODS: Semi-structured interviews were conducted with 12 medical students upon their return from an IHE. A purposive sampling strategy was used. Inductive data analysis using a constant comparative technique generated initial codes which were later organised into higher-order themes. RESULTS: Five themes relating to ethical issues were identified: (i) uncertainty about how best to help; (ii) perceptions of Western medical students as different; (iii) moving beyond one's scope of practice; (iv) navigating different cultures of medicine, and (v) unilateral capacity building. CONCLUSIONS: International health electives are associated with a range of ethical issues for students. Students would benefit from formal pre-departure training, which should include an evaluation of their expectations of and motivations for participating in an IHE, careful selection of the IHE from amongst the opportunities available, learning about the local context of the IHE prior to departure, and the exploration and discussion of ethical and professionalism issues. Other factors that would benefit students include having an invested onsite colleague or supervisor, maintaining an ongoing connection with the home institution, and formal debriefing on conclusion of the IHE.
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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 | 0.014 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".