Bibliographic record
Abstract
OBJECTIVES: This article examines sedentary behaviours (television viewing, computer use and reading) in relation to obesity among Canadian adults aged 20 to 64 years. METHODS: The analysis is based on 42,612 respondents from the 2007 Canadian Community Health Survey Cross-tabulations were used to compare the prevalence of obesity by time engaged in sedentary behaviours. Multiple logistic regression models were used to determine if associations between sedentary behaviours and obesity were independent of the effects of sociodemographic variables, leisure-time physical activity and diet. RESULTS: Approximately one-quarter of men (25%) and women (24%) who reported watching television 21 or more hours per week were classified as obese. The prevalence of obesity was substantially lower for those who averaged 5 or fewer hours of television per week (14% of men and 11% of women). When examined in multivariate models controlling for leisure-time physical activity and diet, the associations between time spent watching television and obesity persisted for both sexes. Frequent computer users (11 or more hours per week) of both sexes had increased odds of obesity, compared with those who used computers for 5 or fewer hours per week. Time spent reading was not related to obesity.
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.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".