Farm Exposures and Allergic Disease Among Children Living in a Rural Setting
Bibliographic record
Abstract
Objectives The purpose of this study was to examine the association between farm exposures and asthma and allergic disease in children while also highlighting the experiences of non-farm rural children.Methods This was a cross-sectional analysis of data collected from across the province of Saskatchewan, Canada in 2014. Surveys were completed by parents of 2275 rural dwelling children (farm and non-farm) aged 0 to 17 years within 46 rural schools. Questionnaires were distributed through schools for parents to complete.Results Asthma prevalence was 7.6%, of which 29.5% of cases were allergic. After adjustment for potential confounders, home location (farm vs non-farm) and other farm exposures were not associated with asthma and asthma phenotypes. Those who completed farm safety education were more likely to have asthma (11.7% vs. 6.7%; p = .001) compared to children without asthma. In sub-analyses among 6–12-year-old children, boys were more likely to have asthma (non-allergic) and use short-acting beta-agonists compared to girls. Doing farm work in the summer was associated with an increased risk of asthma [adjusted OR (aOR) = 1.71 (1.02–2.88); p = .041]. Doing routine chores with large animals was associated with an increased risk of asthma [aOR = 1.83 (1.07–3.15); p = .027] and allergic asthma [aOR = 2.37 (95%CI = 1.04–5.40); p = .04].Conclusion The present study showed that the prevalence of asthma and asthma phenotypes were similar between farm and non-farm rural children. There did not appear to be differential involvement in farming activities between those with and without asthma although those with asthma had more training suggesting possible attempts to mitigate harm from farm exposures.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".