Impact of Excessive Daytime Sleepiness on The Safety and Health of Farmers in Saskatchewan
Bibliographic record
Abstract
BACKGROUND: Sleep disorders may negatively impact the health and well-being of affected individuals. The resulting sleepiness and impaired cognitive functioning may also increase the risks for injury. OBJECTIVE: To examine the relationship between daytime sleepiness, defined as an Epworth Sleepiness Scale score >10, and self-reported sleep apnea, as potential determinants of farming-related injury and self-perceived physical health. METHODS: Phase 2 of the Saskatchewan Farm Injury Cohort Study (2013) involved a baseline survey that included 2849 individuals from 1216 farms. A mail-based questionnaire was administered to obtain self-reports regarding sleep, demographics, farm injuries and general physical health. Multilevel logistic regression was used to quantify relationships between excessive daytime sleepiness and health. RESULTS: The prevalence of excessive daytime sleepiness was 15.1%; the prevalence of diagnosed sleep apnea was 4.0%. Sleepiness was highest in the 60 to 79 (18.7%) and ≥80 (23.6%) years of age groups, and was higher in men (19.0%) than in women (9.3%). Injuries were reported by 8.4% of individuals, and fair or poor health was reported by 6.2%. Adjusting for confounding, individuals with excessive daytime sleepiness appeared more likely to experience a farming-related injury (OR 1.34 [95% CI 0.92 to 1.96]) and were more likely to report poorer physical health (OR 2.19 [95% CI 1.45 to 3.30]) than individuals with normal daytime sleepiness. CONCLUSION: Excessive daytime sleepiness, a potentially treatable condition, appeared to be common in farmers and to negatively affect their health. Sleep disorder diagnosis and treatment programs did not appear to be used to their full potential in this population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".