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Record W3215901866 · doi:10.1123/jsep.2021-0169

Application of the Multi-Process Action Control Model to Predict Physical Activity During Late Adolescence

2021· article· en· W3215901866 on OpenAlexaffabout
Matthew Kwan, Denver M. Y. Brown, Pallavi Dutta, Imran Haider, John Cairney, Ryan E. Rhodes

Bibliographic record

VenueJournal of Sport and Exercise Psychology · 2021
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsMcMaster UniversityUniversity of VictoriaBrock University
Fundersnot available
KeywordsPsychologyReflexivityCoping (psychology)Developmental psychologyVariance (accounting)Physical activityHabitStructural equation modelingSocial psychologyAction (physics)Clinical psychologyPhysical therapyMedicineStatistics

Abstract

fetched live from OpenAlex

The aim of this study was to apply the Multi-Process Action Control model to examine how the additions of regulatory and reflexive processes predict physical activity (PA) behaviors among adolescents. Our sample included 1,176 Grade 11 students (Mage = 15.85 ± 0.38) recruited from a large school board in Southern Ontario. Participants completed a questionnaire including measures of self-reported PA and PA cognitions derived from the Multi-Process Action Control model. Results found the reflective process explaining 16.5% of the variance in PA, with the additions of regulatory and reflexive processes significantly improving the explained variance by 5.1% and 8.2%, respectively. Final models revealed coping planning (estimate = 45.10, p = .047), identity (estimate = 55.82, p < .001), and habit (estimate = 64.07, p < .001) as significant predictors of PA. Findings reinforce the need for integrative models to better understand PA, with coping planning, habit formation, and development of an active identity to be salient targets for intervention during adolescence.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.222
Threshold uncertainty score0.280

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.054
GPT teacher head0.418
Teacher spread0.365 · 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

Citations13
Published2021
Admission routes2
Has abstractyes

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