A prospective sequential analysis of the fear-avoidance model of pain
Bibliographic record
Abstract
The primary purpose of this study was to analyze the sequential relationships proposed by the fear-avoidance model of pain [Vlaeyen JWS et al. The role of fear of movement/(re)injury in pain disability. J Occup Rehab 1995;5:235-52]. Specifically, this study evaluated whether early change in catastrophizing predicted late change in fear of movement, and whether these factors influenced post-treatment return-to-work. Secondary analyses tested relationships between (1) early change in catastrophizing, late change in depression, and disability; and (2) early change in catastrophizing, late change in pain severity, and disability. Analyses were conducted on a sample of 121 individuals (82 men and 32 women) with a work-related musculoskeletal injury, and high baseline catastrophizing and fear of movement scores. Participants were enrolled in a 10-week community-based disability management intervention, and they completed measures of catastrophizing, fear of movement, depression and pain severity at pre-, mid- and post-treatment. Return-to-work was assessed 4 weeks following termination of the intervention. Contrary to predictions, results from correlational analyses revealed non-significant relationships among indices of early change in catastrophizing and late changes in fear of movement, depression and pain severity. Multiple logistic regression analyses revealed that early change in catastrophizing, late changes in fear of movement and late change in pain severity were significant predictors of return-to-work, while late changes in depression were not. These findings highlight the importance of reductions in psychosocial risk factors in augmenting return-to-work outcomes. Implications for the fear-avoidance model and future research 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".