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Record W1133614578 · doi:10.1080/13548506.2015.1058486

Understanding physical activity in individuals with prediabetes: an application of social cognitive theory

2015· article· en· W1133614578 on OpenAlexafffund
Lorian Taylor, Kim D. Raine, Ronald C. Plotnikoff, Jeff K. Vallance, Arya M. Sharma, John C. Spence

Bibliographic record

VenuePsychology Health & Medicine · 2015
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsAthabasca UniversityUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsPrediabetesCoping (psychology)Social cognitive theoryDemographicsPsychologySelf-efficacyCognitionType 2 diabetesPsychological interventionClinical psychologyPopulationMedicineDevelopmental psychologyGerontologyDiabetes mellitusDemographySocial psychologyEndocrinologyPsychiatryEnvironmental health

Abstract

fetched live from OpenAlex

Despite well-documented evidence implicating physical activity (PA) in the prevention of type 2 diabetes, the overwhelming majority of individuals with prediabetes are not physically active enough. The purpose of this study was to investigate the applicability of the social cognitive theory (SCT) in understanding PA behaviour in individuals with prediabetes. Individuals with prediabetes (N = 232) completed a mailed questionnaire assessing demographics, self-reported PA (MET.min/wk) and SCT constructs for PA MET.min/wk. For PA MET.min/wk, scheduling and task efficacy both had significant effects on PA (β = .30 and .22, respectively). Goal formation also had a direct effect on PA for scheduling, coping and task efficacy (β = .20, .34 and .30, respectively). Task, coping and scheduling efficacy explained a significant portion of the variance in PA behaviour. Overall, SCT appears to have merit as a model for understanding PA in individuals with prediabetes. Further evaluative inquiry is needed to establish support for the use of the SCT as a framework for developing, implementing and evaluating PA behaviour change interventions in this population.

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)0.002
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 score0.478
Threshold uncertainty score0.517

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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)0.0000.000

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 head0.291
GPT teacher head0.530
Teacher spread0.239 · 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

Citations12
Published2015
Admission routes2
Has abstractyes

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