The Ethical Implications of Student Participation in Short-Term Experiences in Global Health
Bibliographic record
Abstract
Student interest in short-term experiences in global health (STEGHs) has grown in recent years. Typically, students travel from high-income countries (HIC), standing to benefit from varied clinical exposure and participation beyond what is allowed in their home jurisdictions. Whether STEGHs benefit the hosting low- and middle-income countries (LMICs) are less clear. We seek to mitigate the ethical implications of student participation in STEGHs by examining three domains: (a) the medical scope of practice for students; (b) cultural competency; and (c) reciprocal partnerships. These domains permit further elucidation of key issues relating to ethics, and public policy, to clarify how STEGH initiatives can be evaluated. Our analysis implicates that STEGHs can be described as post-colonial enterprises that perpetuate structural violence in LMICs and may cultivate hubris and savior complexes in learners. Examination of these domains necessitates two points for further analysis and evaluation with respect to STEGHs: (1) a stronger idea of justice and distributive justice that includes considerations of structural violence and reciprocity; and (2) realignment of student global health priorities towards the communities being cared for as opposed to the benefits of high income countries and volunteers. To improve global health knowledge transfer worldwide, we advocate for reciprocity between HIC and LMIC institutions and training in the colonial history of global health. We hope these changes will reduce eurocentrism within student global healthand any potential harms.
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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 | 0.035 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.021 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".