Specific and general therapeutic mechanisms in cognitive behavioral treatment of chronic pain.
Bibliographic record
Abstract
OBJECTIVE: Many studies document efficacy of cognitive behavioral therapy (CBT) for chronic pain, but few studies have examined potential treatment mechanisms. In analyses of data from a controlled trial, we examined whether changes in attitudes toward adopting a pain self-management approach-CBT-specific mechanisms-and quality of working alliance and patient expectations-general mechanisms-early in treatment were related to later-treatment changes in outcomes. METHOD: Our sample was composed of 94 adults (primarily White; mean age: 55.3 years, SD = 11.7; 23% female) who participated in enhanced or standard CBT, and completed measures of attitudes toward self-management (mechanisms), pain intensity, pain interference, depressive symptoms and goal accomplishment (outcomes) at pretreatment, 4- and 8-week assessments, and posttreatment. Working alliance was measured at 4 and 8 weeks, and patient expectations at 3 weeks. RESULTS: Because the CBT conditions produced comparable improvements, we combined them. Precontemplation and action attitudes toward pain self-management showed significant quadratic trends over assessments such that 67% and 94.1% (respectively) of total pre-post changes occurred in the first 4 weeks. Outcomes showed only significant linear trends. Cross-lagged regressions revealed that pretreatment-to-4-week changes in action attitudes and 4-week levels of working alliance were related significantly with 4-week-to-posttreatment changes in pain intensity and interference but not vice versa and that 3-week patient expectations were related to 4-week-to-posttreatment changes in interference. Analyses in which mechanism factors were entered simultaneously revealed nonsignificant unique effects on outcomes. CONCLUSIONS: Adopting an action attitude early in treatment may represent a specific CBT mechanism but with effects held largely in common with 2 general mechanisms.
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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 | 0.002 | 0.002 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".