Evaluation of Group and Individual Change in a Multidisciplinary Pain Management Program
Bibliographic record
Abstract
OBJECTIVES: Systematic reviews have consistently shown that multidisciplinary interventions are more effective than waitlist and other unimodal active treatments for a range of chronic pain conditions. However, these group-based statistics fail to inform us whether these programs result in clinically meaningful improvement at the individual level. The current study examines group changes and individual responsiveness to a CBT-informed multidisciplinary chronic pain management program. METHODS: The analyses are based on data obtained from 263 outpatients. In addition to examining group-based treatment effects, we evaluated individual responsiveness to the program using 3 different criteria for assessing clinically important change. RESULTS: Statistically significant improvement was found for all measures at posttreatment, with effect sizes ranging from small to medium. Gains were largely maintained at follow-up. The results of the clinically important change analysis revealed that not everyone improved uniformly, and the magnitude of change varied across the 3 different methods. This variability in the extent of improvement prompted further analyses in an attempt to identify individual differences that could predict responsiveness to treatment. No differences were found between responders and nonresponders to treatment. DISCUSSION: The results of our study are consistent with previous research, and highlight the potential for multidisciplinary programs to improve the well-being of individuals with chronic pain. Clinically important change analyses underscore the variability that exists in chronic pain patients and allows for a more fine grained evaluation of individual responsiveness to treatment. Considering the strengths and limitations of each methodological approach for assessing clinically important change, guidelines are offered for future research and program development.
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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 | 0.076 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".