Alcohol use among immigrant Latino farmworkers in North Carolina
Bibliographic record
Abstract
BACKGROUND: Heavy alcohol use among Latino farmworkers is believed to be common, but it remains poorly documented. METHODS: Interviewer-administered survey questionnaires were administered to 151 Latino male farmworkers in Eastern North Carolina; topics included frequency of alcohol consumption, amount of alcohol typically consumed, frequency of consuming five or more drinks in a single setting, and signs of alcohol abuse/dependence. RESULTS: Over one-quarter (26%) completely abstain from alcohol, but 27% report frequent heavy drinking, or drinking five or more alcoholic beverages two or more times per month. Over one-third of farmworkers (39%) may be alcohol-dependent. Frequent heavy drinking is more common among single farmworkers and among those who do not follow the crops, and dependence is more common among farmworkers living in camps comprised of both H2A and non-contract workers. CONCLUSIONS: There is substantial variation in alcohol use among Latino farmworkers. Although a substantial proportion of farmworkers abstain from alcohol, an equally substantial proportion report alcohol use that poses significant risk for injury on and off the job. More research is needed to document the potential threat to Latino farmworkers by their alcohol use. Interventions targeting alcohol are needed to minimize the risk of injury in an already dangerous occupation.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".