Longitudinal Analysis Supports a Fear-Avoidance Model That Incorporates Pain Resilience Alongside Pain Catastrophizing
Bibliographic record
Abstract
BACKGROUND: The fear-avoidance model of chronic pain holds that individuals who catastrophize in response to injury are at risk for pain-related fear and avoidance behavior, and ultimately prolonged pain and disability. PURPOSE: Based on the hypothesis that the predictive power of the fear-avoidance model would be enhanced by consideration of positive psychological constructs, the present study examined inclusion of pain resilience and self-efficacy in the model. METHODS: Men and women (N = 343) who experienced a recent episode of back pain were recruited in a longitudinal online survey study. Over a 3-month interval, participants repeated the Pain Resilience Scale, Pain Catastrophizing Scale, Tampa Scale of Kinesiophobia, Pain Self-Efficacy Questionnaire, the McGill Pain Questionnaire, and NIH-recommended measures of pain, depressive symptoms, and physical dysfunction. Structural equation modeling assessed the combined contribution of pain resilience and pain catastrophizing to 3-month outcomes through the simultaneous combination of kinesiophobia and self-efficacy. RESULTS: An expanded fear-avoidance model that incorporated pain resilience and self-efficacy provided a good fit to the data, Χ2 (df = 14, N = 343) = 42.09, p = .0001, RMSEA = 0.076 (90% CI: 0.05, 0.10), CFI = 0.97, SRMR = 0.03, with higher levels of pain resilience associated with improved 3-month outcomes on measures of pain intensity, physical dysfunction, and depression symptoms. CONCLUSIONS: This study supports the notion that the predictive power of the fear-avoidance model of pain is enhanced when individual differences in both pain-related vulnerability (e.g., catastrophizing) and pain-related protective resources (e.g., resilience) are considered.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".