Motivation for behavior change in patients with chest pain
Bibliographic record
Abstract
Purpose To assess the effect of diagnostic testing for coronary artery disease (CAD) on motivation for change, and on lifestyle change for patients with chest pain. Design/methodology/approach This observational study followed patients with chest pain suggestive of CAD for three years. Constructs of autonomous and controlled motivation for lifestyle change, autonomous orientation, and autonomy support from self‐determination theory were assessed. Self‐reported tobacco use, physical activity, and diet were assessed at baseline and three years later. Physician rating of pre‐ and post‐test probability of CAD were also assessed. CAD diagnosis was established after three years. Findings Physicians' autonomy‐supportive style and patients' autonomous orientations both predicted greater patient autonomous motivation, which in turn predicted improved diet, more exercise, and marginally less smoking. High probability of CAD also led patients to become more autonomously motivated for lifestyle change. Research limitations/implications The observational nature of the study and the self‐report measures of health behaviors preclude causal conclusions from this study. Findings from this study suggest that patient motivation and risk behavior are affected by results of cardiac testing, by physicians' support of autonomy, and by patients' personalities. Practical implications Physicians may be effective in motivating behavior change around time of testing for CAD. Originality/value The self‐determination theory model for health behavior change accounted for change in patient health risk behavior change around the time of testing for CAD. Physicians and researchers might use these results to design and test interventions for practitioners to effectively motivate behavior change around the time of medical tests.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".