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Record W1978530004 · doi:10.1080/16506073.2011.573572

Why Do They Exercise Less? Barriers to Exercise in High-Anxiety-Sensitive Women

2011· article· en· W1978530004 on OpenAlexafffund
Brigitte C. Sabourin, Catherine A. Hilchey, Marie-josée Lefaivre, Margo C. Watt, Sherry H. Stewart

Bibliographic record

VenueCognitive Behaviour Therapy · 2011
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsNorth York General HospitalUniversity of New BrunswickSt. Francis Xavier UniversityDalhousie University
FundersCanadian Institutes of Health Research
KeywordsAnxiety sensitivityAnxietyPhysical exercisePsychologyMental healthClinical psychologyMediationPhysical fitnessPhysical activityGerontologyMedicinePhysical therapyPsychiatry

Abstract

fetched live from OpenAlex

Anxiety sensitivity (AS; fear of arousal sensations) is a risk factor for mental and physical health problems, including physical inactivity. Because of the many mental and physical health benefits of exercise, it is important to better understand why high-AS individuals may be less likely to exercise. The present study's aim was to understand the role of barriers to exercise in explaining lower levels of physical exercise in high-AS individuals. Participants were undergraduate women who were selected as high (n = 82) or low (n = 72) AS. High-AS women participated in less physical exercise and perceived themselves as less fit than low-AS women. Mediation analyses revealed that barriers to exercise accounted for the inverse relationships between AS group and physical exercise/fitness levels. Findings suggest that efforts to increase physical exercise in at-risk populations, such as high-AS individuals, should not focus exclusively on benefits to exercise but should also target reasons why these individuals are exercising less.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.066
GPT teacher head0.351
Teacher spread0.284 · 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 source (direct Gemma or distilled Codex), 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

Citations59
Published2011
Admission routes2
Has abstractyes

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