Managing lapses in cardiac rehabilitation exercise therapy: Examination of the problem-solving process.
Bibliographic record
Abstract
PURPOSE/OBJECTIVE: Poor adherence to cardiac rehabilitation (CR) exercise therapy is an ongoing problem. Problem-solving (PS) is an identified cognitive-behavioral strategy to promote exercise adherence. However, PS process has not been examined, and how PS promotes adherence is not known. Using Social Cognitive Theory and Ewart's Social Problem-Solving Model as guiding frameworks, we examined proposed theoretical links between persistence, an indicator of adherence, and (a) PS effectiveness and (b) self-regulatory efficacy. Based on the Model of Social Problem-Solving, 2 distinct components of the PS process (problem-solving and solution implementation), were examined. RESEARCH METHOD/DESIGN: Older adult participants (N = 52; 32 men) representing a typical CR sample (mean age = 65.6 years; SD = 10.8) participated in this correlational, observational study. RESULTS: Two hierarchical multiple regressions indicated that PS effectiveness and self-regulatory efficacy were significant predictors of anticipated persistence. Relative to PS process, both predictors accounted for: (a) 41% of the variance in anticipated persistence with PS; and (b) 49% of the variance in anticipated persistence with solution implementation. CONCLUSIONS/IMPLICATIONS: Proposed theoretical relationships were supported, and both PS effectiveness and self-regulatory efficacy accounted for a greater amount of the variance in anticipated persistence than either alone. Future efforts to improve adherence to rehabilitative exercise could include the use of PS. The 2 distinct components of the PS process may be important for successful adjustment to problems.
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 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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".