“They Will Come to Understand”: Supervisor Reflections on International Medical Electives
Bibliographic record
Abstract
Phenomenon: Increasing numbers of medical students from high-income countries are undertaking international medical electives (IMEs) during their training. Much has been written about the benefits of these experiences for the student, and concerns have been raised regarding the burden of IMEs on host communities. The voices of physicians from low- and middle-income countries who supervise IMEs have not been explored in depth. The current study sought to investigate host–physician perspectives on IMEs. Approach: Host supervisors were recruited by convenience sampling through students travelling abroad for IMEs during the summer of 2012. From 2012 through 2014, 11 semistructured interviews were conducted by telephone with host supervisors from Nepal, Uganda, Ghana, Guyana, and Kenya. Participants were invited to describe their motivations for hosting IMEs and their experiences of the benefits and harms of IMEs. Interviews were transcribed verbatim and checked for accuracy. An initial coding framework was developed and underwent multiple revisions, after which analytic categories were derived using conventional qualitative content analysis. Findings: For host supervisors, visits from international medical students provided a window into the resource-rich medical practice of high-income countries, and supervisors positioned themselves, their education, and clinical expertise against perceived standards of the international students' context. Hosting IMEs also contributed to supervisors' identities as educators connected to a global community. Supervisors described the challenge of helping students navigate their distress when confronting global health inequity. Finally, the desire for increasingly reciprocal relationships was expressed as a hope for the future. Insights: IMEs can be formative for host supervisors' identities and are used to benchmark host institutions compared with international medical standards. Reciprocity was articulated as essential for IMEs moving forward.
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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 | 0.013 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.010 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.013 |
| 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".