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Record W1996879611 · doi:10.1007/s12160-011-9264-2

Patterns of Motivation and Ongoing Exercise Activity in Cardiac Rehabilitation Settings: A 24-Month Exploration from the TEACH Study

2011· article· en· W1996879611 on OpenAlexafffund
Shane N. Sweet, Heather Tulloch, Michelle Fortier, Andrew Pipe, Robert D. Reid

Bibliographic record

VenueAnnals of Behavioral Medicine · 2011
Typearticle
Languageen
FieldMedicine
TopicCardiac Health and Mental Health
Canadian institutionsUniversity of Ottawa
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsRehabilitationHealth psychologyIntervention (counseling)PsychologyPhysical therapyPhysical activityIntrinsic motivationSelf-efficacyPhysical medicine and rehabilitationMedicinePublic healthPsychotherapistSocial psychologyPsychiatryNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Few studies have explored exercise and motivational patterns of cardiac rehabilitation patients in the long term. PURPOSE: We explored differential patterns of exercise and motivation in cardiac rehabilitation patients over a 24-month period and examined the relationship between these emerging patterns. METHODS: Participants (n = 251) completed an exercise, barrier self-efficacy, outcome expectations and self-determined motivation questionnaire. Latent class growth modelling was used to classify patients in different exercise and motivational patterns. RESULTS: Three exercise patterns emerged: inactive, non-maintainers and maintainers (16%, 67% and 17% of sample per pattern, respectively). Multiple trajectories were found for barrier self-efficacy, outcome expectations and self-determined motivation (3, 5, and 4, respectively). Patients in high barrier self-efficacy, outcome expectation and self-determined groups had greater probability of being in the maintainer exercise group. CONCLUSIONS: Identifying a patient's exercise and motivational profile could help cardiac rehabilitation programmes tailor their intervention to optimize the potential for continued exercise activity.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.976

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 studies0.0000.000
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 head0.156
GPT teacher head0.405
Teacher spread0.249 · 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

Citations50
Published2011
Admission routes2
Has abstractyes

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