Social class, gender, and time use: implications for the social determinants of body weight?
Bibliographic record
Abstract
BACKGROUND: The social gradient in body weight (for example, obesity) departs from the social gradient in other health outcomes. Innovative approaches are needed to understand the observed patterns. This study examines time-use patterns by indicators of socio-economic position, and considers the implications of variations in time use for the social gradient in weight reported in other studies. DATA AND METHODS: The data are from respondents aged 25 to 64 to Canada's 1986 and 2005 General Social Surveys, which focused on time use. Participation in various activities was examined by sex, and by personal income and education, stratified by sex, in both years. RESULTS: Higher-income men and women were more likely than those of lower income to spend time in paid work, commuting and eating out, and less likely to spend time sleeping. Men and women with higher education were more likely than those with lower education to spend time in physical activity (2005 only) and reading. These time-use patterns plausibly contribute to the social gradient in obesity reported in other Canadian studies. INTERPRETATION: The findings suggest that there is value in looking beyond a narrow range of health behaviours toward broader measures of daily routines to gain insight into the social determinants of weight and health.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".