Temporal Relations Between Pain Catastrophizing and Adverse Health and Mental Health Outcomes After Whiplash Injury
Bibliographic record
Abstract
OBJECTIVES: Pain catastrophizing has been shown to be a prognostic indicator for pain severity and the co-occurrence of mental health conditions such as depression and post-traumatic stress disorder after whiplash injury. However, the pattern of available findings is limited in its implications for the possible "antecedent" or "causal" role of pain catastrophizing. The purpose of the present study was to examine the temporal relations between pain catastrophizing, pain severity, depressive symptoms, and post-traumatic stress symptoms (PTSS) in individuals receiving treatment for whiplash injury. MATERIALS AND METHODS: The sample consisted of 388 individuals enrolled in a multidisciplinary program for whiplash injury. Participants completed self-report measures of pain catastrophizing, pain severity, depressive symptoms, and PTSS at the time of admission, mid-treatment (4 week), and treatment completion (7 week). A cross-lagged panel analysis was used to examine the temporal relations between pain catastrophizing, pain severity, depressive symptoms, and PTSS across all 3 timepoints. RESULTS: Model fit was acceptable after the inclusion of modification indices. Pain catastrophizing at the time of admission predicted all other variables at 4 weeks. Pain catastrophizing at 4 weeks also predicted all other variables at 7 weeks. In addition, some bidirectional relations were present, particularly for variables assessed at week 4 and week 7. DISCUSSION: Findings support the view that pain catastrophizing might play a transdiagnostic role in the onset and maintenance of health and mental health conditions. The findings call for greater emphasis on the development of treatment techniques that target pain catastrophizing in intervention programs for whiplash injury.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".