Development and testing of the rheumatoid arthritis quality of care survey
Bibliographic record
Abstract
OBJECTIVES: The Rheumatoid Arthritis (RA) Quality of Care Survey (RAQCS) was developed to measure care quality according to a previously developed national RA quality improvement framework. METHODS: The development of the RAQCS occurred over 3 phases. First, the survey was developed by a team of healthcare providers, researchers, and two patient partners based on the existing national quality framework's 21 performance measures (PMs) and strategic objectives. Second, cognitive debriefing interviews were conducted with individuals living with RA to identify survey clarity, appropriateness of survey questions, and response options. Third, the survey was revised and distributed to participants recruited from Rheum4U (rheumatology longitudinal cohort). Results were tabulated and compared with a chart audit of participant medical records. RESULTS: Fifty-three participants completed the RAQCS. High performance (i.e., ≥70% meeting PM) was observed for 13 of 20 PMs. Lower performance was seen for the remaining PMs, which included documentation of body mass index (BMI) and smoking status, discussion of physical activity goals, comorbidity management including risk assessments for cardiovascular health and fragility fractures and disease activity assessment. There was high agreement (≥70%) between the RAQCS and chart review for 9 of 20 PMs. CONCLUSIONS: High agreement was observed between the RAQCS and chart review for selected PMs. The RAQCS may also be a valuable tool for quality improvement for measures where data are not usually available through other sources. Further testing of the RAQCS is needed to ascertain its reliability and validity as a patient self-reported tool to measure RA care quality.
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 | 0.057 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".