Development and validation of Arabic version of the pain catastrophizing scale
Bibliographic record
Abstract
Introduction: The pain catastrophizing scale (PCS) is the most widely used tool to assess pain catastrophizing. The aim of this study was to translate, culturally adapt, and validate the PCS questionnaire in Arabic.Methods: A systematic translation process was used to translate the original English PCS into Arabic. After the pilot study, we validated our version among patients with chronic pain at two tertiary care centers. We tested the reliability of our version using internal consistency and test-retest reliability. We examined the validity by assessing construct validity, concurrent validity (by investigating the associations with Brief Pain Inventory [BPI]), and face validity.Results: A total of 113 subjects (50 men, 63 women) were included in the study. Cronbach's α was 0.94 (95% confidence interval [CI]: 0.92–0.96), and interclass correlation coefficients was 0.83 (95% CI: 0.77–0.89) for the total scale. There was no statistically significant difference in the total PCS scores between patients who reported experiencing current pain and those who did not. Among patients who reported having current pain, pain severity was weakly associated with the total PCS scores (r = 0.22, P = 0.03). PCS and its subscales were not statistically significantly associated with any of the BPI items. Nonetheless, patients who were diagnosed with neuropathic pain had statistically significantly higher scores on the total PCS, rumination, and helplessness subscales. Most patients found the PCS questions to be clear and easy to understand, and thought the questionnaire items covered all their problem areas regarding their pain catastrophizing.Conclusion: Our translated version of PCS is reliable and valid for use among Arabic-speaking patients.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".