Host community perspectives on trainees participating in short‐term experiences in global health
Bibliographic record
Abstract
CONTEXT: High-income country (HIC) trainees are undertaking global health experiences in low- and middle-income country (LMIC) host communities in increasing numbers. Although the benefits for HIC trainees are well described, the benefits and drawbacks for LMIC host communities are not well captured. OBJECTIVES: This study evaluated the perspectives of supervising physicians and local programme coordinators from LMIC host communities who engaged with HIC trainees in the context of the latter's short-term experiences in global health. METHODS: Thirty-five semi-structured interviews were conducted with LMIC host community collaborators with a US-based, non-profit global health education organisation. Interviews took place in La Paz, Bolivia and New Delhi, India. Interview transcripts were assessed for recurrent themes using thematic analysis. RESULTS: Benefits for hosts included improvements in job satisfaction, local prestige, global connectedness, local networks, leadership skills, resources and sense of efficacy within their communities. Host collaborators called for improvements in HIC trainee attitudes and behaviours, and asked that trainees not make promises they would not fulfil. Findings also provided evidence of a desire for parity between the opportunities afforded to US-based staff and those available to LMIC-based partners. CONCLUSIONS: This study provides important insights into the perspectives of LMIC host community members in the context of short-term experiences in global health for HIC trainees. We hope to inform the behaviour of HIC trainees and institutions with regard to international partnerships and global health activities.
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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 | 0.006 | 0.010 |
| 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 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".