Short-Term Global Health Education Programs Abroad: Disease Patterns Observed in Haitian Migrant Worker Communities Around La Romana, Dominican Republic
Bibliographic record
Abstract
The possibility of encountering rare tropical disease presentations is commonly described as a benefit derived by developed world medical trainees participating in clinical service-oriented short-term global health experiences in the developing world. This study describes the health status of a population served by a short-term experience conducted by a North American institute, and the results of a retrospective review are used to identify commonly encountered diseases and discuss their potential educational value. Descriptive analysis was conducted on 1,024 encounter records collected over four unique 1-week-long trips by a North American institution serving Haitian migrant workers in La Romana, Dominican Republic. The top five diagnoses seen in the clinic were gastroesophageal reflux disease (GERD), hypertension (HTN), upper respiratory infections, otitis media, and fungal skin infection. On occasion, diagnoses unique to an indigent tropical population were encountered (e.g., dehydration, malnutrition, parasites, and infections.). These findings suggest a similarity between frequently encountered diagnoses on a short-term clinical service trip in Dominican Republic and primary care presentations in developed world settings, which challenges the assumption that short-term service experiences provide exposure to rare tropical disease presentations. These findings also represent additional data that can be used to better understand the health and healthcare planning among this vulnerable population of Haitian migrant workers.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".