MétaCan
Menu
Back to cohort
Record W2089009428 · doi:10.1080/01490400802685948

Understanding Physical Inactivity: Prediction of Four Sedentary Leisure Behaviors

2009· article· en· W2089009428 on OpenAlexaff
Ryan E. Rhodes, Rachel N. Dean

Bibliographic record

VenueLeisure Sciences · 2009
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsTheory of planned behaviorPsychologySedentary behaviorPsychological interventionSample (material)Physical activityReading (process)Social psychologyRegression analysisOrdinary least squaresDevelopmental psychologyControl (management)Applied psychologyComputer scienceMedicine

Abstract

fetched live from OpenAlex

An understanding of the motives underlying sedentary leisure behavior may guide interventions to decrease these behaviors. The purpose of this study was to apply the theory of planned behavior (TPB) to understand the motives underlying four common sedentary leisure activities: television viewing, computer use, reading/music, and socializing. A cross-sectional community sample of 206 adults and 174 undergraduate students completed measures of the TPB of these four leisure behaviors and self-reported behavior. Results using ordinary least squares regression provided evidence that sedentary behaviors may be intentional and planned with a primary attitude base but not related to perceived behavioral control. The findings provide information about sedentary behavior motivation and support the validity of the TPB for the prediction of these behaviors.

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)0.001
metaresearch head score (Gemma)0.008
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 score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.268
GPT teacher head0.430
Teacher spread0.161 · 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

Citations76
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueLeisure SciencesSame topicBehavioral Health and InterventionsFrench-language works237,207