Clinimetric properties of the Nepali version of the Pain Catastrophizing Scale in individuals with chronic pain
Bibliographic record
Abstract
Background: Pain catastrophizing is an exaggerated negative cognitive response related to pain. It is commonly assessed using the Pain Catastrophizing Scale (PCS). Translation and validation of the scale in a new language would facilitate cross-cultural comparisons of the role that pain catastrophizing plays in patient function. Purpose: The aim of this study was to translate and culturally adapt the PCS into Nepali (Nepali version of PCS [PCS-NP]) and evaluate its clinimetric properties. Methods: We translated, cross-culturally adapted, and performed an exploratory factor analysis (EFA) of the PCS-NP in a sample of adults with chronic pain (N=143). We then confirmed the resulting factor model in a separate sample (N=272) and compared this model with 1-, 2-, and 3-factor models previously identified using confirmatory factor analyses (CFAs). We also computed internal consistencies, test–retest reliabilities, standard error of measurement (SEM), minimal detectable change (MDC), and limits of agreement with 95% confidence interval (LOA 95% ) of the PCS-NP scales. Concurrent validity with measures of depression, anxiety, and pain intensity was assessed by computing Pearson’s correlation coefficients. Results: The PCS-NP was comprehensible and culturally acceptable. We extracted a two-factor solution using EFA and confirmed this model using CFAs in the second sample. Adequate fit was also found for a one-factor model and different two- and three-factor models based on prior studies. The PCS-NP scores evidenced excellent reliability and temporal stability, and demonstrated validity via moderate-to-strong associations with measures of depression, anxiety, and pain intensity. The SEM and MDC for the PCS-NP total score were 2.52 and 7.86, respectively (range of PCS scores 0–52). LOA 95% was between -15.17 and +16.02 for the total PCS-NP scores. Conclusion: The PCS-NP is a valid and reliable instrument to assess pain catastrophizing in Nepalese individuals with chronic pain. Keywords: measurement error, outcome measure, Bland–Altman plot, reliability, measurement properties, pain assessment
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.049 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".