Validation of the Coping with Health Injuries and Problems questionnaire in a longitudinal cohort with recent-onset RA
Bibliographic record
Abstract
Abstract Objective To validate the Coping with Health Injuries and Problems (CHIP) questionnaire in a prospective cohort of early RA patients. Methods Between 2006 and 2022, newly diagnosed RA patients self-administered CHIP at baseline and at follow-up visits. The original CHIP comprises four subscales (Distraction, Palliative, Instrumental, Emotional preoccupation), each containing eight items (scores 8 to 40). At inclusion and again after more than 2 years of follow-up, internal consistency was assessed with Cronbach’s alpha, factor structure with exploratory and confirmatory factor analyses (EFA, CFA), and sensitivity to change with mixed linear models with repeated measures. Results In 381 early RA patients, the means (SD) were 23.75 (6.52) for Distraction, 23.55 (6.11) for Palliative, 31.38 (5.41) for Instrumental and 25.11 (7.89) for Emotional preoccupation, values comparable to the literature only available in back pain patients. In 253 of the 381 patients followed up into established RA, all subscales except Distraction had decreased significantly between inclusion and follow-up. Internal consistency was similar in established and early RA (Cronbach’s alphas: 0.77 to 0.89 vs 0.75 to 0.86, respectively). EFA in early and established RA suggested that three items linked to treatment adherence consistently segregated from other Instrumental items as a subscale, although this did not improve internal consistency and CFA significantly. Conclusion The original CHIP possesses good psychometric properties to describe individual coping styles in both early and established RA. Coping in RA might be better characterized using five rather than four subscales, with the additional subscale addressing treatment adherence.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".