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Record W4400613969 · doi:10.1080/14927713.2024.2378802

Beyond the big five: a 10-year longitudinal study of personality traits predicting leisure-time physical activity among adults

2024· article· en· W4400613969 on OpenAlexaffvenue
Vinu Selvaratnam, Alex T. Silver, Steven E. Mock

Bibliographic record

VenueLeisure/Loisir · 2024
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsBig Five personality traitsPsychologyPersonalityLeisure timeLongitudinal studyPhysical activityLeisure activityDevelopmental psychologySocial psychologyStatisticsMedicineMathematicsPhysical therapy

Abstract

fetched live from OpenAlex

The purpose of this study was to compare and contrast the Big Five model of personality with Tellegen’s Multidimensional Personality Questionnaire (MPQ) to investigate how they predict light intensity, moderate intensity and vigorous intensity leisure-time physical activity (LTPA) using population-based secondary longitudinal data. The Tellegen facet harm avoidance predicted a decrease in moderate and vigorous LTPA. From the Big Five, extraversion predicted increased moderate and vigorous LTPA and, additionally, openness predicted increased vigorous LTPA. This comparison reveals the complementary nature of both personality models to better understand how multiple aspects of personality predict LTPA and shows that leisure studies scholars may want to consider alternatives to the Big Five when considering intrapersonal predictors of physically active leisure.

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)0.002
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 score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
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 head0.040
GPT teacher head0.326
Teacher spread0.285 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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