Longitudinal Analysis Supports a Fear-Avoidance Model That Incorporates Pain Resilience Alongside Pain Catastrophizing
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it