Results from 10 Years of a CBT Pain Self-Management Outpatient Program for Complex Chronic Conditions
Bibliographic record
Abstract
Background.Traditional unimodal interventions may be insufficient for treating complex pain, as they do not address cognitive and behavioural contributors to pain. Cognitive Behavioural Therapy (CBT) and physical exercise (PE) are empirically supported treatments that can reduce pain and improve quality of life.Objectives.To examine the outcomes of a pain self-management outpatient program based on CBT and PE at a rehabilitation hospital in Toronto, Ontario.Methods.The pain management group (PMG) consisted of 20 sessions over 10 weeks. The intervention consisted of four components: education, cognitive behavioural skills, exercise, and self-management strategies. Outcome measures included the sensory, affective, and intensity of pain experience, depression, anxiety, pain disability, active and passive coping style, and general health functioning.Results.From 2002 to 2011, 36 PMGs were run. In total, 311 patients entered the program and 214 completed it. Pairedt-tests showed significant pre- to posttreatment improvements in all outcomes measured. Patient outcomes did not differ according to the number or type of diagnoses. Both before and after treatment, women reported more active coping than men.Discussion.The PMGs improved pain self-management for patients with complex pain. Future research should use a randomized controlled design to better understand the outcomes of PMGs.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".