Perspectives on Safety and Health Among Migrant and Seasonal Farmworkers in the United States and México: A Qualitative Field Study
Bibliographic record
Abstract
CONTEXT: A large number of hired farmworkers in the United States come from México. Understanding safety and health concerns among the workers is essential to improving prevention programs. PURPOSE: The purpose of this pilot study was to obtain detailed information about safety and health concerns of hired farmworkers in Colorado and in México. METHODS: A total of 10 migrant farmworkers from northern Colorado and 5 seasonal farmworkers from Guanajuato, México, were interviewed using a semi-structured interview process. The social cognitive theory (SCT) served as a framework to gain understanding of safety and health among workers. FINDINGS: Topics of concern identified included causes of farm, home and motor vehicle injuries, and treatment preferences for injuries and illnesses. Four main themes emerged: safety and health concerns, personal control and prevention strategies, factors affecting control and prevention strategies, and the importance of family. CONCLUSIONS: Further study of the themes using a revised semi-structured interview will be done in a larger study among hired farmworkers. The results add to the current work to understand specific health and safety concerns among these workers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".