Measuring the impact and distress of osteoarthritis from the patients' perspective
Bibliographic record
Abstract
BACKGROUND: To assess the internal construct validity of the Perceived Impact of Problem Profile (PIPP), a patient based outcome measure based on the International Classification of Functioning, Disability and Health (ICF), which assesses impact and distress, in an osteoarthritis (OA) cohort. METHODS: A questionnaire comprising the 23-item PIPP, which assesses five domains (mobility, participation, self care, psychological well being and relationships), the Western Ontario McMasters University Osteoarthritis Index (WOMAC), the General Well-Being Index (GWBI), and the Hospital Anxiety and Depression Scale (HADS) was posted to people with clinician diagnosed OA. Assessment of the internal construct validity of the PIPP was undertaken using Rasch analysis performed with RUMM2020 software and concurrent validity through comparator measures. RESULTS: Two hundred and fifty-nine participants with OA responded. Analysis of the five individual domains of the PIPP indicated that there was good fit to the Rasch model, with high person separation reliability. One item required removal from the Mobility subscale and the Participation subscale. There were strong correlations between the PIPP Mobility scores and the WOMAC disability and pain subscales (rho = .73 and rho = .68), and between the PIPP Psychological well-being and HADS Depression (rho = .71) and GWBI (rho = -.69). High inter-correlations between the impact and distress subscales for each domain (range rho = .85 to .96), suggested redundancy of the latter. CONCLUSION: This study demonstrates that the PIPP has good psychometric properties in an OA population. The PIPP, using just the impact subscales, provides a brief, reliable and valid means of assessing the impact of OA from the individual's perspective and operationalizing the bio-psychosocial model by the application of a single multi-domain questionnaire.
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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 | 0.003 | 0.012 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".