The complex relationships involved in global health: a qualitative description
Bibliographic record
Abstract
BACKGROUND: Growing numbers of medical trainees now participate in global health experiences (GHEs) during their training. To enhance these experiences we sought to explore expectations inherent in the relationships between GHE stakeholder groups. METHODS: 20 open-ended, semi-structured interviews probed participant perceptions and assumptions embedded in GHEs. A fundamental qualitative descriptive approach was applied, with conventional content analysis and constant comparison methods, to identify and refine emerging themes. Thematic structure was finalized when saturation was achieved. Participants all had experience as global health participants (10 trainees, 10 professionals) from an urban, academic, Canadian medical centre. RESULTS: We identified three stakeholder groups: participants (trainees and professionals), host communities, and sponsoring institutions. During interviews, four major themes emerged: (i) cultural challenges, (ii) expectations and perceptions, (iii) relationships and communication, and (iv) discordant objectives. Within each theme, participants recurrently described tensions existing between the three stakeholder groups. CONCLUSIONS: GHE participants frequently face substantial tensions with host communities and sponsoring agencies. Trainees are particularly vulnerable as they lack experience to navigate these tensions. In the design of GHEs, the needs of each group must be considered to ensure that benefits outweigh potential harms. We propose a conceptual model for developing educational objectives that acknowledge all three GHE stakeholder groups.
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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 | 0.020 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".