Patient Satisfaction With Chronic Pain Management: Patient Perspectives of Improvement
Bibliographic record
Abstract
Integrating satisfaction measures with pain-related variables can highlight global change and improvement from the patients’ perspective. This study examined patient satisfaction in an interdisciplinary chronic pain management program. Nine hundred and twenty-seven (n = 927) participants completed pre- and post-treatment measures of pain, depression, catastrophizing, anxiety, stages of change, and pain acceptance. Multiple regression was used to examine these variables at admission and discharge as predictors of patient satisfaction. Pain-related variables explained 50.6% of the variance (R 2 = .506, F 22,639 = 29.79, P < .001) for general satisfaction, and 38.9% of the variance (R 2 = 0.389, F 22,639 = 18.49, P < .001) for goal accomplishment. Significant predictors of general satisfaction included depression (β = −0.188, P < .001) and the maintenance stage of change (β = 0.272, P < .001). The latter was also a significant predictor of goal accomplishment (β = 0.300, P < .001). Discharge pain-related measures are more influential than admission measures for predicting patient satisfaction. Patient satisfaction is significantly related to establishing a self-management approach to 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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".