Assessing the Short-Term Global Health Experience: A Cross-Sectional Study of Demographics, Socioeconomic Factors, and Disease Prevalence
Bibliographic record
Abstract
Interest in short-term global health experiences to underserviced populations has grown rapidly in the last few decades. However, there remains very little research on what participants can expect to encounter. At the same time, it has been suggested that in order for physicians and workers to provide safe and effective care, volunteers should have a basic understanding of local culture, health systems, epidemiology, and socioeconomic needs of the community before arriving. Our objective was to add to the limited literature on what short-term global health trips can expect to encounter through a cross-sectional study of patient demographics, socioeconomic markers, and the prevalence of diseases encountered on a short-term medical service trip to Lima, Peru. Descriptive analysis was conducted on clinic data collected from patients living in Pamplona Alta and Pamplona Baja, Lima, Peru, in July 2015. We found that volunteers encountered mainly female patients (70.8%), and that there were significant socioeconomic barriers to care including poverty, poor housing, environmental exposures, and lack of continuity of health care. Analysis of the disease prevalence found a high proportion of acute and chronic musculoskeletal pain in the adult populations (18.8% and 11.4%, respectively), and a high presentation of upper respiratory tract infections (25.4%) and parasites (22.0%) in the pediatric group. These findings can be used by future short-term medical service trips to address potential gaps in care including the organization of weekend clinics to allow access to working men, and the use of patient education and nonpharmacological management of acute and chronic disease.
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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 | 0.002 | 0.003 |
| 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 | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".