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Record W3107786593 · doi:10.1177/0890117120976059

Uneven Effects of Adverse Weather Conditions on Participation in Leisure-Time Physical Activities Across Income Levels

2020· article· en· W3107786593 on OpenAlexaboutno aff
Jingye Shi, Yuting Wang

Bibliographic record

VenueAmerican Journal of Health Promotion · 2020
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsAdverse weatherRentingSubsidyEnvironmental healthProbit modelDemographyBusinessEnvironmental scienceDemographic economicsMedicineGeographyMeteorologyEconomicsEngineering

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.801
Threshold uncertainty score0.384

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.049
GPT teacher head0.411
Teacher spread0.362 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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