Uneven Effects of Adverse Weather Conditions on Participation in Leisure-Time Physical Activities Across Income Levels
Bibliographic record
Abstract
PURPOSE: To examine how the effect of adverse weather on participation in leisure-time physical activities (LTPA) varies with income. DESIGN: Cross-sectional study. SUBJECTS: 14,394 individuals from 56 Canadian cities, surveyed in 1992, 1998, and 2005. MEASURES: The adverseness level of daily weather is measured by the number of hours with precipitation or strong winds (wind speeds in excess of 38 km/hour) between 6 am and 11 pm. ANALYSIS: Probit and multinomial logit models are used to examine the variation in weather-LTPA correlations across income levels. RESULTS: At the mean income level, when the weather quality deteriorated from all-day nice weather to all-day adverse weather, the probability of participating in LTPA decreased by 24.54% (from 0.2424 to 0.1829, P < 0.01). As income increased by $10,000, the same deterioration in weather quality led to a 17.06% decrease in LTPA (from 0.2508 to 0.2080, P < 0.01). The smaller decrease is mainly because the $10,000 increase in income is associated with a 14.49% increase in indoor LTPA, which partly offsets the decrease in outdoor LTPA. CONCLUSION: Interventions and policies that increase indoor physical activity options, such as providing easier access to indoor facilities and offering subsidies for purchasing or renting home exercise equipment, are promising for effectively promoting LTPA, especially for individuals in lower-income groups or from regions that frequently experience adverse weather.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".