Catastrophizing and pain-related fear predict failure to maintain treatment gains following participation in a pain rehabilitation program
Bibliographic record
Abstract
The present study explored whether pain-related psychosocial risk factors played a role in determining whether treatment gains were maintained following participation in a rehabilitation intervention for musculoskeletal injury. The study sample consisted of 310 individuals (163 women, 147 men) with work-related musculoskeletal conditions who were enrolled in a physical rehabilitation program. Measures of pain severity, pain catastrophizing and pain-related fear were completed at the time of admission and at the time of discharge. Pain severity was assessed again at 1-year postdischarge. Participants were classified as "recovered" if they showed a decrease in pain of at least 2 points and rated their pain at discharge as less than 4/10. Recovered participants were considered to have failed to maintain treatment gains if their pain ratings increased by at least 2 points from discharge assessment to 1-year follow-up, and they rated their pain as 4/10 or greater at 1-year follow-up. The results of a logistic regression revealed that participants with high posttreatment scores on measures of catastrophizing and fear of pain were at increased risk of failing to maintain treatment gains. The findings suggest that unless end-of-treatment scores on catastrophizing and fear of pain fall below the risk range, treatment-related reductions in pain severity may not be maintained in the long term. The clinical and theoretical implications of the findings are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".