Use of complementary theories to examine exercise adherence among cardiac rehabilitation initiates
Bibliographic record
Abstract
Complementary use of theories may provide more information about adherence to exercise therapy than a single theory. Top-down theories (Self-Efficacy Theory: SET) outline cognitive processes that affect behavior. Bottom-up theories (Common Sense Model: CSM) consider individuals' appraisals that influence behavior. Both SET and CSM were used in complimentary fashion to examine psychological aspects of cardiac rehabilitation (CR) initiates' exercise adherence. A prospective design was used to examine participants during the 3-month initiation phase of a standard CR program. Our first purpose was to determine if strength of CSM illness perceptions (IP) could classify CR initiates to stronger/weaker IP groups. Our second purpose was to detect any differences between IP groups on SET variables and 3-month exercise adherence. Participants ( N =49) completed the IP Questionnaire, self-regulatory efficacy and negative outcome expectations (OEs) scales. At CR onset, cluster analysis successfully classified participants to weaker (n = 21) and stronger (n = 28) IP groups. Groups differed significantly on the IPs of illness identity, consequences, and emotion ( p = .0001). The stronger IP group had higher negative OEs ( p = .03) and was less adherent to CR exercise after 3 months ( p = .04). This study identifies new psychological differences relative to CR initiates' adherence to exercise by using complimentary theories. Acknowledgments: CIHR MSc & Canada Research Chair awards
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".