Predictive validity of the Chronic Pain Coping Inventory in subacute low back pain
Bibliographic record
Abstract
The Chronic Pain Coping Inventory (CPCI) was developed to assess eight behavioral coping strategies hypothesized to be important for pain adaptation. But the predictive validity of the CPCI has yet to be tested in a longitudinal study. Here, 321 workers on sick leave after a work accident affecting the low back pain (LBP) region completed the CPCI during the subacute stage (T1) of LBP as well as the Catastrophizing scale of the Coping Strategies Questionnaire (CSQ). Disability, pain intensity and depressive mood were assessed simultaneously as well as 6 months later (T2). Work status was also determined at follow-up. Hierarchical regression analyses revealed that the CPCI (Guarding scale) predicted T1 disability and T2 disability (Relaxation scale), but T1 disability was the best predictor of T2 disability. For T1 pain intensity, the CSQ's Catastrophizing dimension was the best predictor and the CPCI Guarding scale added a small contribution. T1 pain intensity was the best predictor of T2 pain intensity. Catastrophizing and Guarding were the most strongly associated with depressive mood at T1 but at T2, only depressive mood at T1 predicted this same variable. Results indicated also that the Guarding and Catastrophizing scales were able to predict future work status. The present study clearly reveals the usefulness of Guarding from the CPCI and Catastrophizing from the CSQ, when predicting different outcomes of adjustment to low back 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 | 0.002 | 0.009 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".