Farmers, mechanized work, and links to obesity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 it