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Record W2531604179 · doi:10.1123/jsep.2016-0256

Should We Be Looking at the Forest or the Trees? Overall Psychological Need Satisfaction and Individual Needs as Predictors of Physical Activity

2016· article· en· W2531604179 on OpenAlexafffund
Jennifer Brunet, Katie E. Gunnell, Pedro J. Teixeira, Catherine M. Sabiston, Mathieu Bélanger

Bibliographic record

VenueJournal of Sport and Exercise Psychology · 2016
Typearticle
Languageen
FieldPsychology
TopicMotivation and Self-Concept in Sports
Canadian institutionsVitalité Health NetworkUniversity of TorontoChildren's Hospital of Eastern OntarioOttawa HospitalUniversité de SherbrookeMontfort HospitalUniversity of Ottawa
FundersFondation de la recherche en santé du Nouveau-Brunswick
KeywordsPsychologyOperationalizationSelf-determination theoryAutonomyCompetence (human resources)Social psychologyPhysical activityApplied psychologyPhysical therapyMedicine

Abstract

fetched live from OpenAlex

The objectives of this study were to examine whether (a) measures designed to assess satisfaction of competence, autonomy, and relatedness needs in physical activity contexts can represent both general and specific needs satisfaction and (b) the specific needs are associated with concurrent moderate-to-vigorous-intensity physical activity (MVPA) participation (Time 1) and MVPA participation 4 months later (Time 2), beyond general psychological need satisfaction (PNS). Data from 544 adolescents ( M age = 14.1 years, SD = 0.6) were analyzed. A bifactor model specifying four factors (i.e., one general PNS and three specific needs) provided a good fit to the data. Extending the model to predict Time 1 and Time 2 MVPA participation also provided a good fit to the data. General PNS and specific needs had unique and empirically distinguishable associations with MVPA participation. The bifactor operationalization of PNS provides a framework to delineate common and distinctive antecedents and outcomes of general PNS and specific needs.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.096
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.053
GPT teacher head0.342
Teacher spread0.290 · 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.

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

Citations51
Published2016
Admission routes2
Has abstractyes

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