Perception of the Quality of Life of Tobacco Growers Exposed to Pesticides: Emphasis on Health, Hearing, and Working Conditions
Bibliographic record
Abstract
Introduction Tobacco farming exposes workers to various health risks due to the high application of pesticides needed to control pests, weeds and fungal diseases that prevent the tobacco plant growth. Objective To analyze the perception of the quality of life of tobacco growers exposed to pesticides, with emphasis on general health, hearing, and working conditions. Method This is a descriptive, cross-sectional study using a quantitative approach with farmers from southern Brazil. Data were collected from November of 2012 to November of 2014. For data collection, we opted for the 36-item short form health survey (SF-36) questionnaire, and a questionnaire with closed questions about health, hearing and working conditions. We evaluated a total of 78 subjects; the study group, made up of 40 tobacco farmers exposed to pesticides, and a control group of 38 participants without occupational exposure to pesticides. Both groups are residents of the same municipality, and users of the federal public health system. Results The results showed that tobacco growers had lower quality of life scores compared with the control group. Significant differences were observed in the areas of pain and general health. There were correlations between physical elements and chronic diseases; hearing complaints and a lack of personal protective equipment use, occupation and hearing complaints, as well as general health and hearing complaints. Conclusion Tobacco farming is a risky activity for general and hearing health, and it can impact the quality of life of those working in this field.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".