Is patient attachment an important consideration when examining support and efficacy dynamics in exercise-based cardiac rehabilitation?
Bibliographic record
Abstract
Engaging in exercise-based cardiac rehabilitation (CR) consistently reduces post-event cardiac risk and improves quality of life. Promoting patients' exercise self-efficacy is key as low self-efficacy inhibits self-care, prolonging symptoms and elevating risk. Support from a partner (spouse or caregiver) is seen as an important predictor of successful rehabilitation outcomes. Partner's perceptions of a patient's capabilities, known as other-efficacy, may also be important, as they may relate to the support partners provide. The current study explored whether partner's other-efficacy was related to how they supported the patient and in turn how this predicted patient's self-efficacy. Further, this project extended work in this area by drawing from attachment theory to examine whether the patient's attachment style may moderate the relationship between support and self-efficacy. CR patient and partner dyads (N=68) were recruited from a 10-week CR program, completing measures of self-efficacy, other efficacy, support, and attachment styles. The overall model was tested using an SPSS macro called PROCESS. Overall mediation was not found; however, other-efficacy was negatively associated with overprotective support (b=-.01, p=.03), and overprotective support was negatively associated with patient self-efficacy (b=-5.03, p=.02). A significant interaction was found between overprotective support and attachment anxiety when predicting patient's self-efficacy (b=4.47, p=.005), indicating that the self-efficacy of patients lower in attachment anxiety was more strongly negatively associated with overprotective support offered by the partner. The findings of this study provide insight into the dynamics between efficacy perceptions, support and attachment, which may help to inform tailoring of education and assistance in partner-based CR.Acknowledgments: SSHRC Small Institutions Grant
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".