DOES RESIDENCE SETTING INFLUENCE HEALTH AND FITNESS?
Bibliographic record
Abstract
Regional differences in health may be related to lifestyle and these differences may impact lifestyle interventions. PURPOSE: We explored Canadian regional and urban-rural associations among lifestyle characteristics (levels of fitness, energy expenditure) and perceived benefits/barriers to adoption of physical activity in older adults. METHODS: A total of 305 healthy community dwelling older adults were examined at baseline in the STEP study, an exercise prescription and counselling intervention conducted in communities throughout Canada. Setting included four regions (Maritimes, Ontario, Alberta, British Columbia) stratified for urban-rural residence in Canada. Thirty-seven phyaicians, 17 urban-20 rural, participated in the study. Outcome measures were the region of Canada, urban or rural residence; cardiorespiratory fitness; energy expenditure (7-day PAR); and exercise benefits and barriers (EBBS). RESULTS: Overall, mean VO2max values between regions were the lowest in the Ontario and highest in Western provinces. In Western provinces, rural setting showed higher energy expenditure than urban setting, whereas in Ontario and Maritimes, urban setting had higher energy expenditure than rural. Perceived benefits of exercise were low overall, however urban setting showed greater perceived benefit than their rural counterparts. Urban-rural difference in perceived barriers to exercise was greatest in western Canada. CONCLUSION: Lifestyle habits related to fitness and physical activity and perceptions of benefit/barrier vary with regional and urban-rural distribution. The impact of these differences on response to physical activity intervention will be explored in the STEP study. Supported by CIHR and Pfizer Canada Inc.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".