Validation of the Chronic Pain Acceptance Questionnaire (CPAQ) in an Internet sample and development and preliminary validation of the CPAQ-8
Bibliographic record
Abstract
This study investigated the psychometric properties of the Chronic Pain Acceptance Questionnaire (CPAQ) in a mixed chronic pain, Internet sample and sought to develop a valid and reliable short form. Questionnaires were completed by 428 respondents, comprising a sample accessed via the Internet (n=319) and a sample who completed a paper and pencil version of the measures (n=109). Using confirmatory factor analysis (CFA) the two-factor structure of the CPAQ in the Internet sample was supported, though a good model fit was only achieved following the removal of one item. The resultant 19 item CPAQ demonstrated good reliability and evidence of validity was obtained for this sample. Data from the Internet sample were used to derive an eight-item short form. The two four-item factors (activity engagement [AE] and pain willingness [PW]) were confirmed using CFA and found to be invariant across both samples with good scale reliability. Higher CPAQ-8 and subscale scores were correlated with less depression and anxiety, pain severity and pain interference, and fewer medical visits for pain. Using structural equation modelling both subscales were found to partially mediate the impact of pain severity on pain interference and emotional distress. In this model AE had stronger associations with outcomes while PW accounted for a small portion of the variance in pain interference and anxiety, but not depression. This study confirmed the two-factor structure of the CPAQ in a mixed chronic pain Internet sample and provides preliminary evidence for the psychometric soundness of the CPAQ-8.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.028 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".