Farmers, mechanized work, and links to obesity
Bibliographic record
Abstract
OBJECTIVE: In a contemporary sample of Saskatchewan farm people, to relate the degree of mechanized and also non-mechanized farm work to the occurrence of being overweight or obese. Secondarily to determine the prevalence of being overweight or obese, and to compare these prevalence levels with those reported for general populations. METHOD: Cross-sectional analyses of baseline survey data provided for 2849 individuals (2619 adults) from 1216 Saskatchewan farms in 2013. Age/sex-standardized prevalence levels of overweight and obesity were compared between the farm cohort and general populations. Durations of specific types of work were described by metabolic equivalent scoring. Multi-level binomial regression was used to study relations between mechanized and also non-mechanized farm work with overweight and obesity. RESULTS: Overall, 65.1% of the adult farm cohort was overweight (39.6%) or obese (25.5%), with prevalence levels that exceeded estimated norms for Canada but not the province of Saskatchewan. Increases in risks for obesity were related to higher amounts of mechanized but not non-mechanized farm work. CONCLUSION: While the mechanization of farm work has obvious benefits in terms of productivity, its potential effects on risks for overweight and obesity must be recognized.
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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) | 0.004 | 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".