Host perspective on academic supervision, health care provision and institutional partnership during short‐term electives in global health
Bibliographic record
Abstract
CONTEXT: Studies about the impact of global health electives on host institutions are scarce and often made from the perspective of institutions that send students. The present research examined the impact of short-term electives in global health (STEGHs) from the under-represented perspective of host institutions in Benin. METHODS: The authors conducted 30 semi-structured interviews from a convenience sample of Beninese health care professionals who had hosted Canadian medical students. Interviewees had previously supervised STEGHs in one of the five different institutions. A subsequent qualitative thematic analysis methodology was used to compilate codes and generate themes. RESULTS: Hosting STEGH students motivated respondents to increase their medical knowledge through self-driven learning. They perceived an improvement in the quality of their care and felt a negligible impact on patient safety. They negatively commented on the lack of clear pedagogic objectives that they could rely on. Interviewees think current STEGH partnerships do not advantage them because institutions that send students offer little support during the electives. Furthermore, sending institutions do not offer the same opportunity for local medical students or professionals to take part in such electives outside of Benin. CONCLUSIONS: Although host health care professionals evaluated global health electives positively overall, specific improvements could mitigate their negative impacts and help create a more balanced partnership between sending and host institutions. Sending institutions could involve host institutions in curriculum planning. They could invest in building reciprocal elective programmes to receive students from elsewhere. Meanwhile they can maximise the transfer of relevant medical knowledge, and provide expertise, resources and support during the electives.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".