Differences in Occupational, Transportation, Domestic, and Leisure-Time Physical Activities: Do Geographical Location and Socio-Cultural Status Matter?
Bibliographic record
Abstract
BACKGROUND: Researchers have recently expressed their concern for the health of Francophones and rural dwellers in Canada. Their levels of physical activity may explain part of the observed differences. However, little is known about the physical activity levels of these 2 groups. The purpose of this study was to assess levels of physical activity among a sample of Francophones and rural dwellers. The study also assessed the associations of various types of physical activity to measures of health status. METHODS: A quota-based convenience sample of 256 adults from Northern Ontario was surveyed using the IPAQ and the SF-12. RESULTS: There were no significant differences in activity levels between language groups (P = .06) or geographical groups (P = .22) on the combined dependent variables based on MANOVA. Leisure-time physical activity scores were consistently associated to better physical component summary scores of the SF-12. CONCLUSIONS: Implications for practice include that leisure-time physical activities have been at the forefront of public health promotion, and our findings support this approach. Further, population specific interventions are indeed important, however, within this Canadian context when identifying target groups one must look beyond sociocultural status or geographical location.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".