A psychometric evaluation of the Pain Response Preference Questionnaire in a chronic pain patient sample.
Bibliographic record
Abstract
OBJECTIVE: The Pain Response Preference Questionnaire (PRPQ) assesses preferences regarding pain-related social support. The initial factor analytic study of the PRPQ conducted with a nonclinical sample identified scales labeled Solicitude, Management, Suppression, and Encouragement. The first aim of the current study was to evaluate whether these scales would be appropriate for use with chronic pain patients. The construct validity of the emerging scales and their relations to pain-related disability were also investigated. METHODS: A large sample of chronic pain patients (N = 300) completed the PRPQ along with self-reports of personality constructs, coping styles, pain severity, and disability. RESULTS: Factor analysis supported a three-factor solution. Two factors were similar to those obtained in its initial evaluation, and were given the identical labels of Solicitude and Suppression. The remaining factor was labeled Activity Direction and was comprised primarily of items that were part of the earlier Management and Encouragement scales. Internally consistent PRPQ scales based on these factors were created. Correlation analyses involving the personality and coping measures provided support for the construct validity of these PRPQ scales. Supportive of their clinical utility, multiple regression analyses indicated that, after adjusting for pain severity, the PRPQ scales accounted for significant variance in disability ratings. CONCLUSIONS: Additional factor analytic research aimed at identifying the most appropriate set of PRPQ scales is warranted. The present findings indicate that the PRPQ scales used in the current study have strong psychometric properties and hold promise as research and clinical tools.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".