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Record W2162122824 · doi:10.1123/jpah.2012-0107

Who Uses Exercise as a Coping Strategy for Stress? Results From a National Survey of Canadians

2014· article· en· W2162122824 on OpenAlexaffabout
John Cairney, Matthew Kwan, Scott Veldhuizen, Guy Faulkner

Bibliographic record

VenueJournal of Physical Activity and Health · 2014
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsCentre for Addiction and Mental Health
Fundersnot available
KeywordsCoping (psychology)Logistic regressionOddsCross-sectional studyPhysical activityPopulationMedicinePsychologyClinical psychologyGerontologyEnvironmental healthPhysical therapy

Abstract

fetched live from OpenAlex

PURPOSE: To examine the prevalence of exercise as a coping behavior for stress, compare this to other coping behaviors, and examine its demographic, behavioral, and health correlates in a nationally representative sample of Canadians. METHOD: We used data from the Canadian Community Health Survey 1.2, a cross-sectional survey of 36,984 Canadians aged 15 and over, and conducted univariate and logistic regression analyses to address our objectives. RESULTS: 40% of Canadians reported using exercise for coping with stress (ranked 8th overall). These individuals were more likely to endorse other 'positive' coping strategies and less likely to use alcohol or drugs for coping. Being younger, female, unmarried, of high SES, and a nonsmoker were associated with higher likelihoods of using exercise as a coping strategy. High levels of leisure-time physical activity were associated with increased, and heavy physical activity at work with decreased, odds of reporting using exercise for stress coping. CONCLUSIONS: While reported use of exercise for stress coping is common in the general population, it is less so than several other behaviors. Encouraging exercise, particularly in groups identified as being less likely to use exercise for stress coping, could potentially reduce overall stress levels and improve general health and well-being.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.923
Threshold uncertainty score0.950

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.152
GPT teacher head0.413
Teacher spread0.261 · 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

Citations97
Published2014
Admission routes2
Has abstractyes

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