Global health electives: Ethical engagement in building global health capacity
Bibliographic record
Abstract
Purpose: Little is known about the impact medical trainees undertaking global health electives (GHEs) have on host institutions and their communities in low-and middle-income countries. The goal of this study was to explore the relationship dynamics associated with GHEs as perceived by host stakeholders at three sites in sub-Saharan Africa.Method: This case-based interpretive phenomenological study examined stakeholder perspectives in Mwanza, Tanzania, and Mbarara and Rugazi, Uganda, where the University of Calgary, Alberta, Canada has long-standing institutional collaborations. Between September and November 2017, 33 host stakeholders participated in semi-structured interviews and 28 host stakeholders participated in focus group discussions. Participant experiences were analyzed using interpretive phenomenological techniques.Results: The findings revealed that, although GHEs are well-established and a common experience for host stakeholders, their perceptions about who visiting medical trainees (VMTs) are remains indistinct. Participants acknowledged that there are a variety of benefits to GHEs, but overall VMTs appear to benefit the most from this unique learning opportunity. Host stakeholders described significant challenges and burdens of GHEs and recommended ways in which GHEs could be improved.Conclusions: GHEs need to be designed to better embrace ethical engagement and reciprocity with host stakeholders to ensure equity in benefits and responsibilities.
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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 | 0.028 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.033 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.022 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".