The Development and Psychometric Validation of an Arabic-Language Version of the Pain Catastrophizing Scale
Bibliographic record
Abstract
Context. The Pain Catastrophizing Scale (PCS) is the most widely used measure of pain-specific catastrophizing. Objectives. The purpose of the present study was to develop and psychometrically evaluate an Arabic-language version of the PCS. Methods. In Study 1, 150 adult chronic nonmalignant pain patients seeking treatment at a hospital setting completed the PCS-A and a number of self-report measures assessing clinical parameters of pain, symptoms of depression, and quality of life. Study 2 employed a cold pressor pain task to examine the PCS-A in a sample of 44 healthy university students. Results. Exploratory factor analyses suggested a two-factor structure. Confirmatory factor analysis comparing the 2-factor model, Sullivan’s original 3-factor model, and a 1-factor model based on the total score all provided adequate fit to the data. Cronbach’s alpha coefficients across all models met or exceeded accepted standards of reliability. Catastrophizing was associated with higher levels of depression and increased pain intensity and interference. Catastrophizing predicted decreased quality of life, even after controlling for the contribution of gender, employment, depression, and pain interference. PCS-A scores were positively correlated with heightened experimental pain severity and decreased pain tolerance. Conclusion. The present results provide strong support for the psychometric properties of the PCS-A.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".