Health and Social Needs in Three Migrant Worker Communities around La Romana, Dominican Republic, and the Role of Volunteers: A Thematic Analysis and Evaluation
Bibliographic record
Abstract
Objective. For decades, Haitian migrant workers living in bateyes around La Romana, Dominican Republic, have been the focus of short-term volunteer medical groups from North America. To assist these efforts, this study aimed to characterize various health and social needs that could be addressed by volunteer groups. Design. Needs were assessed using semistructured interviews of community and professional informants, using a questionnaire based on a social determinants of health framework, and responses were qualitatively analysed for common themes. Results. Key themes in community responses included significant access limitations to basic necessities and healthcare, including limited access to regular electricity and potable water, lack of health insurance, high out-of-pocket costs, and discrimination. Healthcare providers identified the expansion of a community health promoter program and mobile medical teams as potential solutions. English and French language training, health promotion, and medical skills development were identified as additional strategies by which teams could support community development. Conclusion. Visiting volunteer groups could work in partnership with community organizations to address these barriers by providing short-term access to services, while developing local capacity in education, healthcare, and health promotion in the long-term. Future work should also carefully evaluate the impacts and contributions of such volunteer efforts.
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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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".