Creation and validation of the 4-item BriefPCS-chronic through methodological triangulation
Bibliographic record
Abstract
BACKGROUND: The Pain Catastrophizing Scale (PCS) is a widely used self-report tool to evaluate pain related catastrophizing. The PCS was developed using classical test theory and has been shown to be psychometrically sound among various populations. However, it's current three subscales are rarely used in clinical practice, offering potential for an abbreviated version that reduces administrative burden and can be used to estimate full scale scores, yet is not bound by the inclusion of items from each subscale. Hence, the aim of the current study was to develop a unidimensional abbreviated version of the PCS through findings from qualitative, classical test theory, and newer Rasch analysis. METHODS: The current cross-sectional study used data from the Quebec Pain Registry (n = 5646) to obtain PCS scores of people seeking care at tertiary chronic pain centres. To develop an abbreviated unidimensional tool, items were removed based on triangulation of qualitative review of each item and response, corrected item-total correlations, and Rasch analysis. Confirmatory factor analysis was conducted on the final remaining items to confirm the tool was assessing a single latent construct (catastrophizing). Fit was assessed using the cumulative fit index (CFI), Tucker Lewis Index (TLI), and root-mean-squared error of approximation (RMSEA). RESULTS: After triangulation, a final abbreviated 4-item scale showed adequate model fit with a strong correlation (r > 0.95) with the original scale and properties that were stable across age, sex, cause, and medicolegal status. Additionally, the brief version addressed some problematic wording on some items on the original scale. Both the original and new abbreviated tool were associated with the Beck Depression Inventory and the Brief Pain Inventory at the same magnitude. CONCLUSION: The abbreviated scale may allow for a decrease in administrator burden and greater clinical uptake when a quick screen for exaggerated negative orientation towards pain is needed.
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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 | 0.084 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".