UNDERSTANDING VARIATION IN SUCCESS IN A QUALITY IMPROVEMENT INITIATIVE IN SASKATCHEWAN, CANADA.
Bibliographic record
Abstract
Background Unexplained variation in clinical practice can be an indicator of poor quality of care. In Saskatchewan, Canada the Variation and Appropriateness Working Group (VAWG) engaged three clinical working groups (CWG) to consider the causes of variation in surgical practice. Objectives Using the Model for Understanding Success in Quality Improvement (MUSIQ) framework, we conducted a qualitative study aimed at exploring VAWG's successes and barriers. Methods We used semi-structured interviews, meetings notes, and VAWG project documents as data for understanding the functioning and context of the CWGs. Results Our comparison of the CWGs highlights the similarities and differences between these groups. All groups identified next steps for understanding the root causes of variation; however, even in the CWG that exhibited positive micro contextual attributes and that made the most progress, success was limited due to the external environment common to all three CWGs. The external environment, the Saskatchewan health care system, did not signal that this work was a priority and physicians did not have the structural support needed to fully engage and advance this work. Conclusions Our research highlights the importance of both micro and macro contextual factors critical to the success of physician-driven quality improvement. The MUSIQ framework identifies features of the Saskatchewan context that can be built upon, and areas were significant work and investment is needed to increase the likelihood of success in future projects. Lessons learned are impacting the implementation of subsequent work; however, there are still critical factors to be addressed in Saskatchewan.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.016 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".