Further Evaluation of the Motivational Model of Pain Self-Management: Coping with Chronic Pain in Multiple Sclerosis
Bibliographic record
Abstract
BACKGROUND: Growing evidence suggests that motivation to engage in pain-coping strategies is a key predictor of how well a person adjusts to pain. According to the Motivational Model of Pain Self-Management, readiness to engage in pain self-management behaviors is influenced by beliefs about the importance of the behavior (importance) and the ability to carry out the behavior (self-efficacy). PURPOSE: The purpose of this study was to test the Motivational Model of Pain Self-Management for exercise and task persistence pain-coping behaviors in a sample of 114 individuals with multiple sclerosis and chronic pain. METHODS: Measures included the Multidimensional Pain Readiness to Change Questionnaire-2 and measures of importance, self-efficacy, and coping behavior duration. Tests of mediation were conducted with two path analyses, one for each coping behavior. RESULTS: The effects of importance and self-efficacy beliefs on coping behaviors were mediated or partially mediated by readiness to engage in those behaviors. CONCLUSIONS: These findings provide support for the Motivational Model of Pain Self-Management and have important implications for the development of treatments for chronic pain.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".