Prevalence of Work‐Related Respiratory Symptoms in Iranian Farmers
Bibliographic record
Abstract
BACKGROUND: Animal exposure may be an important trigger for work-related symptoms among farmers. OBJECTIVE: To estimate the prevalence of work-related respiratory symptoms (WRS) in sheep breeders and agricultural farmers and to determine work-related risk factors. METHODS: A family doctor used a questionnaire to interview a cohort of 173 farmers comprised of 127 sheep breeders and 46 agricultural farmers in the rural area of Rokh (northeast Iran). The questionnaire pertained to recurrent wheezing, cough, breathlessness or chronic phlegm while at work (these symptoms define WRS), flu-like illness and physician-diagnosed asthma. RESULTS: There were 71 subjects (41%) with WRS: 10 of 46 agricultural farmers (21.7%) and 61 of 127 sheep breeders (48.0%). The proportions of sheep breeders with wheezing (16.5%), asthma (14%), cough (29%), breathlessness (31.5%) and flu-like illness (38%) were higher than in agricultural farmers. A significant dose-response relationship among the daily hours worked with animals, the number of animals and the prevalence of symptoms was established for sheep farmers. Sheep shearing and the use of pesticide were associated with an increased risk of wheezing and phlegm. CONCLUSIONS: The results suggest that sheep farmers in general have higher rates of work-related symptoms than agricultural farmers. The severity of work-related symptoms will increase with an increase in frequency of animal contact; therefore, these results may underestimate the impact of this exposure.
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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 | 0.000 | 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) | 0.001 | 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".