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Record W1983184055 · doi:10.1080/08870446.2011.531570

Changing exercise through targeting affective or cognitive attitudes

2011· article· en· W1983184055 on OpenAlexafffund
Mark Conner, Ryan E. Rhodes, Ben Morris, Rosemary McEachan, Rebecca Lawton

Bibliographic record

VenuePsychology and Health · 2011
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsUniversity of Victoria
FundersSocial Sciences and Humanities Research Council of CanadaCanadian Institutes of Health ResearchMichael Smith Health Research BCCanadian Diabetes Association
KeywordsPsychologyAffect (linguistics)CognitionClinical psychologyDevelopmental psychologyPsychiatry

Abstract

fetched live from OpenAlex

Two studies investigated the impact of affective and cognitive messages compared to a no-message control on self-reported exercise. Students (Study 1, N = 383 and Study 2, N = 197) were randomly allocated to one of the three conditions (control - no message, affective message or cognitive message). Participants completed questionnaire measures tapping components of the theory of planned behaviour in relation to exercise and reported their level of exercise (3 weeks later). In Study 2, measures of need for affect (NFA) and need for cognition (NFC) were also completed. Results showed that affective messages consistently produced greater increases in self-reported level of exercise than the other conditions. In both studies, this effect was partly mediated by affective attitude change. Study 2 indicated these effects to be significantly stronger among those high in NFA or low in NFC. These findings indicate the value of affective messages that target affective attitudes in changing exercise behaviour.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
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)0.0030.000

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 head0.257
GPT teacher head0.513
Teacher spread0.256 · 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 designNon-randomized trial
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

Citations232
Published2011
Admission routes2
Has abstractyes

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