A Rasch Analysis of the Pain Catastrophizing Scale Supports its Use as an Interval-level Measure
Bibliographic record
Abstract
OBJECTIVES: To evaluate the properties of the Pain Catastrophizing Scale (PCS) from a Rasch paradigm. METHODS: A secondary analysis of 235 patients with work-related pain conditions was performed using the Rasch methodology. Unidimensionality, item fit, location independence, differential item functioning, response option structure, and linearity were evaluated for the 13-item PCS score. RESULTS: Two items (8 and 12) required rescoring to address disordered response thresholds. Significant misfit to the Rasch model was corrected through the use of testlets based on the original 3 factors of the PCS (rumination, magnification, and helplessness). After rescoring and creation of testlets, the scale showed good fit to the Rasch model (χ(2)=6.93, P=0.91) and could be logically considered an interval-level scale. No evidence of differential item functioning was found for sex or location of pain. The items in the scale covered the spectrum of catastrophizing levels reported by the sample. A transformation matrix is presented that allows simple conversion of ordinal to interval-level scores. DISCUSSION: The results of this secondary analysis suggest that the PCS can be appropriately evaluated as an interval-level scale when the composite 13-item score is considered, as has been standard practice to date. Implications for clinical and research use are discussed.
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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 | 0.033 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".