Change Narratives That Elude Quantification: A Mixed-Methods Analysis of How People with Chronic Pain Perceive Pain Rehabilitation
Bibliographic record
Abstract
Chronic pain negatively impacts health, well-being, and social participation. Effective rehabilitation often hinges on long-term changes in pain-related perceptions and behaviors. However, there are important gaps in understanding how patients perceive these changes. The present pilot study addresses this gap by using qualitative and quantitative methodologies to explore how patients perceive and experience changes in function, participation, and pain-related factors following a chronic pain rehabilitation program. A mixed-method design was used in which the core method was qualitative. Descriptive quantitative data was used to further characterize the sample. Semistructured interviews were conducted 1-6 months following treatment completion. Questionnaires were administered before and after treatment and at follow-up. Interview data was analyzed thematically. Participants' individual descriptive data was compared to established cut-scores and criteria for change. A major theme of personal growth emerged in the qualitative analysis. Participants also discussed the factors that facilitated personal growth and the ongoing challenges to this growth. The quantitative data revealed limited improvement on measures of pain, disability, catastrophizing, and depression. These findings suggest that, despite limited improvement on treatment-related questionnaires, patients can experience an important and enduring sense of personal growth. Clinical and theoretical implications are discussed.
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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 | 0.034 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".