Are coping and catastrophising independently related to disability and depression in patients with whiplash associated disorders?
Bibliographic record
Abstract
PURPOSE: The aim is to study how pain coping strategies and catastrophising are related to disability and depression in patients with whiplash-associated disorders (WAD). Specifically, we wanted to test if they are independent predictive variables, after controlling for pain severity, sociodemographic and crash-related variables. METHODS: A convenience sample of 147 patients with WAD of less than 3 months of duration was recruited. They were requested to complete the Pain Catastrophising Scale, the two-item version of the Chronic Pain Coping Inventory and to report sociodemographic and crash-related information, pain intensity, disability and depression. RESULTS: Although several pain coping strategies were related with disability in univariate analyses, only asking for assistance was a marginally significant predictive variable in a multiple regression analysis after controlling for catastrophising. Catastrophising was a significant predictive variable after controlling for pain coping strategies. With depression as the outcome, resting and task persistence were the only pain coping strategies which were related in univariate analyses. However, none of them were predictive variables after controlling for catastrophising. Again, catastrophising was a significant predictive variable after controlling for pain coping strategies. CONCLUSIONS: Our results show that catastrophising about pain is more important than pain coping strategies in patients with WAD of a short duration. These results can contribute to the conceptual distinction between pain coping strategies and catastrophising.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".