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Record W2134247783 · doi:10.1177/1359105309342301

Protection Motivation Theory and Physical Activity

2009· article· en· W2134247783 on OpenAlexafffund
Ronald C. Plotnikoff, Ryan E. Rhodes, Linda Trinh

Bibliographic record

VenueJournal of Health Psychology · 2009
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsUniversity of VictoriaUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsPsychologyCoping (psychology)Psychological interventionPopulationTheory of planned behaviorExplained variationPhysical activityVariance (accounting)Clinical psychologyDevelopmental psychologySocial psychologyDemographyMedicineStatisticsPsychiatry

Abstract

fetched live from OpenAlex

The purpose of this study was to examine the Protection Motivation Theory (PMT) to predict physical activity (PA) behaviour in a large, population-based sample of adults. One thousand six hundred and two randomly selected individuals completed two telephone interviews over two consecutive six-month periods assessing PMT constructs. PMT explained 35 per cent and 20 per cent of the variance in intention and behaviour respectively. Coping cognitions as moderators of threat explained 1 per cent of the variance in intention and behaviour. Age and gender as moderators of threat did not provide additional variance in the models. We conclude that salient PMT predictors (e.g. self-efficacy) may guide the development of effective PA interventions in the general 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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.982
Threshold uncertainty score0.298

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.142
GPT teacher head0.512
Teacher spread0.370 · 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 designOther design
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

Citations49
Published2009
Admission routes2
Has abstractyes

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