Factors Related to Self-perceived Health in Rural Men and Women
Bibliographic record
Abstract
This study examined self-perceived health status among men and women who live on farms, as well as variations in factors related to negative health status observed by gender. Data were collected in the province of Saskatchewan, Canada, in 2013 through the use of a cross-sectional survey. A multistage sample was developed consisting of farms nested within rural municipalities and then agricultural soil zones. The response rate was 48.8% at the farm level, with a final sample of 2,353 (1,416 men, 937 women) from 1,119 farms. Variables under study included self-reports of health status, as well as demographic, behavioral, and farm operational factors that could influence perceived health status. The analysis was initially descriptive followed by multilevel logistic regression analyses. Self-reports of diagnosed comorbidities were strongly associated with negative health status among both men and women. Daytime sleepiness was more modestly associated with negative health status in both genders. Among men, additional risk factors tended to be functional, and included older age, part-time work status, and binge drinking. Among women, additional risk factors included cigarette smoking, overweight or obesity, and lower levels of education. The study demonstrated that there were both similarities and differences between men and women on farms in the factors related to negative self-perceived health status. These findings should inform the content and targeting of health promotion programs aimed at rural populations.
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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".