The Pain Coping Questionnaire short-form: preliminary reliability and validity
Bibliographic record
Abstract
INTRODUCTION: The Pain Coping Questionnaire (PCQ) has support for its validity and reliability as a tool to understand how a child copes with pain of an extended duration. However, measure length may limit feasibility in clinical settings. OBJECTIVES: The primary goal of this study was to develop a short-form (PCQ-SF) that could be used for screening how children cope with chronic or recurrent pain and examine its reliability and validity. METHODS: The PCQ-SF was developed in a stepwise manner. First, a confirmatory factor analysis was computed using an amalgamated data set from the validation studies of the PCQ (N = 1225). Next, ratings from researchers and clinicians were obtained on PCQ item content and clarity (n = 12). Finally, the resulting 16-item short-form was tested in a pediatric sample living with chronic and recurrent pain (65 parent-child dyads; n = 128). RESULTS: The PCQ-SF has acceptable preliminary reliability and validity. Both statistical and expert analyses support the collective use of the 16 items as an alternative to the full measure. CONCLUSIONS: The compact format of the PCQ-SF will allow practitioners in high-volume clinical environments to quickly determine a child's areas of strengths and weaknesses when coping with pain. Future research using larger more diverse samples to confirm clinical validity is warranted.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".