Canadian Rheumatology Association Guidance for Developing and Endorsing Quality Measures to Support Learning Health Systems
Bibliographic record
Abstract
OBJECTIVE: To review methods for developing and endorsing quality measures (QMs) to inform a national quality measurement framework for rheumatology care in Canada. METHODS: We conducted a rapid environmental scan of QM development organizations from Canada, the United Kingdom, the United States, and Australia. Major phases in the development of QMs were abstracted. The results were reviewed and synthesized with members of the Canadian Rheumatology Association (CRA) Digital Measurement Subcommittee through iterative review across 3 virtual meetings. The guidance was approved at the committee and the CRA board level. RESULTS: Five key steps in the measure development cycle are proposed: conceptualization and prioritization, measure specification development, testing and validation, implementation and reporting, and continuous evaluation and maintenance. Foundational to all phases is the engagement of individuals from diverse backgrounds with lived experience of disease, healthcare providers, quality measurement scientists, and partner organizations. Measures should be aligned with domains of quality (effectiveness, efficiency, equity, patient-centeredness, safety and timeliness of care delivery) and be developed transparently. Endorsement of future QMs should, at minimum, prioritize relevance/importance, validity, feasibility, and acceptability and use/usability. CONCLUSION: This guidance document establishes a comprehensive and relevant framework for the development and/or endorsement of QMs in Canadian rheumatology care. This framework will permit streamlining of future quality improvement efforts at the national level.
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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 | 0.194 | 0.354 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.018 | 0.019 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.010 | 0.008 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".