Development and Psychometric Evaluation of a New Measure of Pain‐Related Support Preferences: The Pain Response Preference Questionnaire
Bibliographic record
Abstract
BACKGROUND: Behavioural conceptualizations of chronic pain posit that solicitous responses to pain behaviours are positively reinforcing and play a role in the development of chronic pain and disability. Recent research suggests that studies investigating this model were likely limited by the use of only a few narrowly defined categories of responses to pain behaviour. A measure of preferences regarding pain-related social support has the potential to improve behavioural models of chronic pain by identifying other potentially reinforcing responses to pain behaviour. OBJECTIVE: The Pain Response Preference Questionnaire (PRPQ) was created to assess preferences regarding pain-related social support. The purpose of the present study was to empirically develop PRPQ scales and examine their psychometric properties. METHODS: A large university student sample (n=487) free of chronic pain completed the 39-item PRPQ. Factor analysis was applied to the data from the present sample to empirically develop PRPQ scales. Using a second student sample (n=87), relationships between the PRPQ scales and theoretically related measures were examined to evaluate the construct validity of the scales. Factor analysis supported four factors that reflected preferences for emotional and instrumental support, assistance in managing pain and emotions, having one's pain ignored, and being encouraged to persist with one's activities. Based on this analysis, scales labelled solicitude, management, suppression and encouragement were created. Correlation analyses supported the construct validity of these scales. CONCLUSIONS: The PRPQ is a psychometrically sound measure of preferences of pain-related social support. Research with clinical samples is needed to further evaluate its psychometric properties and clinical utility.
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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 | 0.010 | 0.019 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".