Predicting responses to self-management treatments for chronic pain: application of the pain stages of change model
Bibliographic record
Abstract
Psychological treatments emphasizing a self-management approach have become commonly accepted alternatives to medical interventions for chronic pain. Unfortunately, these approaches often fail to engage a significant portion of targeted individuals and are associated with high drop-out and relapse rates. Informed by the transtheoretical model of behavior change and the cognitive behavioral perspective on chronic pain, the Pain Stages of Change Questionnaire (PSOCQ) was developed to assess readiness to adopt a self-management approach to chronic pain. Initial studies supported the reliability and validity of four distinct scales, Precontemplation, Contemplation, Action and Maintenance. The current study was designed to assess the ability of the PSOCQ to predict self-management participation and outcome. The PSOCQ and several relevant outcome measures were assessed before and after self-management treatment by 109 chronic pain patients. Profile analysis revealed that treatment completers and non-completers differed significantly across the four PSOCQ scales. Post-hoc comparisons indicated that pretreatment PSOCQ Precontemplation and Contemplation scores discriminated these two groups. Separate analyses revealed that Action and Maintenance scores increased over the course of treatment, and that changes in the PSOCQ scales were associated with improved outcomes. These findings suggest that increased commitment to a self-management approach to chronic pain may serve as a mediator or moderator of successful treatment. This study supports the predictive validity and utility of the PSOCQ, as well as the relevance of the stages of change model to self-management of 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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".