Depression, Social Factors, and Farmworker Health Care Utilization
Bibliographic record
Abstract
PURPOSE: Farmworkers frequently live in rural areas and experience high rates of depressive symptoms. This study examines the association between elevated depressive symptoms and health care utilization among Latino farmworkers. METHODS: Data were obtained from 2,905 Latino farmworkers interviewed for the National Agricultural Workers Survey. Elevated depressive symptoms were measured using the Center for Epidemiologic Studies Depression short-form. A dichotomous health care utilization variable was constructed from self-reported use of health care services in the United States. A categorical measure of provider type was constructed for those reporting use of health care. RESULTS: Over 50% of farmworkers reported at least 1 health care visit in the United States during the past 2 years; most visits occurred in a private practice. The odds of reporting health care utilization in the United States were 45% higher among farmworkers with elevated depressive symptoms. Type of provider was not associated with depressive symptoms. Women were more likely to seek health care; education and family relationships were associated with health care utilization. CONCLUSIONS: Latino farmworkers who live and work in rural areas seek care from private practices or migrant/Community Health Clinics. Farmworkers with elevated depressive symptoms are more likely to access health care. Rural health care providers need to be prepared to recognize, screen, and treat mental health problems among Latino farmworkers. Outreach focused on protecting farmworker mental health may be useful in reducing health care utilization while improving farmworker quality of life.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".