Cognitive appraisal and coping in chronic pain patients
Bibliographic record
Abstract
OBJECTIVES: This study analyses the relationships between patients' cognitive appraisals concerning their pain and the coping strategies they use. In addition, the way the coping strategy influences the intensity of perceived pain and impairment in these patients was studied. METHODS: One hundred and twenty two patients with musculoskeletal chronic pain participated. The assessment tools were as follows: The Cognitive Appraisal Inventory for Chronic Pain Patients (CAI), the Vanderbilt Pain Management Inventory (VPMI), the McGill Pain Questionnaire (MPQ) and the Impairment and Functioning Inventory for Chronic Pain Patients (IFI). The hypothetical model was empirically tested using the LISREL 8.20 software package and the unweighted least squares method. RESULTS: High levels of challenge appraisal were associated with low levels of passive coping and high levels of active coping strategies, whereas the harm, loss or threat appraisal predicted high use of passive coping strategies. Passive coping had three statistically significant path coefficients: high levels of passive coping were associated with low levels of functioning and high levels of pain intensity and impairment. However, high levels of active coping reported high levels of daily functioning. DISCUSSION: By analysing the cognitive appraisals made by chronic pain patients, clinicians could make better predictions regarding the way they cope and adjust.
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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 | 0.001 | 0.009 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".