Psychometric Evaluation and Refinement of the Pain Response Preference Questionnaire
Bibliographic record
Abstract
BACKGROUND: The Pain Response Preference Questionnaire (PRPQ) assesses preferences regarding pain-related social support. The initial factor analytical study of the PRPQ produced four empirically supported scales labelled Solicitude, Management, Encouragement and Suppression. A second study produced similar findings, but suggested that the Management and Encouragement scales be combined into a single scale labelled Activity Direction. OBJECTIVES: To use factor analytical methods to evaluate these competing configurations of the PRPQ (ie, three versus four scales) and to further refine the measure. The ability of the PRPQ scales to account for pain severity and disability ratings was also evaluated. METHODS: Chronic pain patients (n=201) completed the PRPQ along with the Pain Catastrophizing Scale (PCS) and self-reports of pain severity and disability. RESULTS: Confirmatory factor analysis indicated that both models tested provided a poor fit to the data. A follow-up exploratory factor analysis was used to further refine the PRPQ scales and resulted in scales labelled Solicitude, Encouragement and Suppression. Supportive of the potential clinical utility of the PRPQ, Suppression was positively associated with pain severity and Solicitude was positively associated with disability. These two scales were also positively associated with the PCS. Supportive of the incremental validity of the PRPQ, a multiple regression analysis indicated that the Solicitude scale accounted for unique variance in disability ratings beyond that accounted for by demographic⁄clinical variables and the PCS. CONCLUSIONS: The PRPQ has promise as a clinical assessment measure and for advancing research examining the interpersonal context of pain.
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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 | 0.025 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".