Characterizing a community health partnership in Dominican Republic: Network mapping and analysis of stakeholder perceptions
Bibliographic record
Abstract
BACKGROUND: Medical trainees complete learning experiences abroad to fulfil global health curricular elements, but this participation has been steadily criticized as fulfilling learner objectives at the cost of host communities. This study uses network and qualitative analyses in characterizing a community coalition in order to better understand its various dimensions and to explore the perceived benefits it provided towards optimizing community outcomes. METHODS: Data from a semi-structured survey was used for network and qualitative analyses. Partner linkages were assessed using network analysis tool UCINET 6 (version 6.6). Thematic analysis was conducted on qualitative responses around the perceived coalition strengths and weaknesses. RESULTS: Network analysis confirmed that local member organizations were key network influencers based on reported formal agreements, general interactions, and information shared. While sharing of resources was rare, qualitative analysis suggested that information sharing contributed to engagement, enthusiasm, and communication that allowed visiting partners to expand their understanding of community needs and shift their focus beyond learner objectives. CONCLUSION: Global health programs for medical students should consider the use of community health coalitions to optimally align the work undertaken by learners on global health experiences abroad. Network mapping can help educators and coalition partners visualize interactions and identify value.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".