Strategies for developing and implementing a rheumatoid arthritis healthcare quality framework: a thematic analysis of perspectives from arthritis stakeholders
Bibliographic record
Abstract
OBJECTIVES: To obtain stakeholder perspectives to inform the development and implementation of a rheumatoid arthritis (RA) healthcare quality measurement framework. DESIGN: Qualitative study using thematic analysis of focus groups and interviews. SETTING: Arthritis stakeholders from across Canada including healthcare providers, persons living with RA, clinic managers and policy leaders were recruited for the focus groups and interviews. PARTICIPANTS: Fifty-four stakeholders from nine provinces. INTERVENTIONS: Qualitative researchers led each focus group/interview using a semistructured guide; the digitally recorded data were transcribed verbatim. Two teams of two coders independently analysed the transcripts using thematic analysis. RESULTS: Perspectives on the use of different types of measurement frameworks in healthcare were obtained. In particular, stakeholders advocated for the use of existing healthcare frameworks over frameworks developed in the business world and adapted for healthcare. Persons living with RA were less familiar with specific measurement frameworks, however, they had used existing online public forums for rating their experience and quality of healthcare provided. They viewed a standardised framework as potentially useful for assisting with monitoring the care provided to them individually. Nine guiding principles for framework development and 13 measurement themes were identified. Perceived barriers identified included access to data and concerns about how measures in the framework were developed and used. Effective approaches to framework implementation included having sound knowledge translation strategies and involving stakeholders throughout the measurement development and reporting process. Clinical models of care and health policies conducive to outcome measurement were highlighted as drivers of successful measurement initiatives. CONCLUSION: These important perspectives will be used to inform a healthcare quality measurement framework for RA.
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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 | 0.105 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".