Extreme Makeover: Can We Achieve Rapid Improvement in Canada's Healthcare System?
Bibliographic record
Abstract
Building upon some key discussion points in the Brown et al. paper, we explore the key elements driving performance measurement and quality improvement strategies in the Veterans Affairs healthcare system in the United States and the national primary-care trusts in England, both of which offer important insights into understanding the factors that affect rapid, large-scale change. In the context of these "extreme makeover" examples, our commentary discusses the currently evolving performance measurement culture in the Canadian primary healthcare reform setting. We specifically highlight the experiences in Saskatchewan, a province that has been acknowledged recently by CIHI as a leader in primary healthcare evaluation. Although Saskatchewan has attempted to overcome the methodological and conceptual challenges in evaluation that Brown et al. outline in their paper, a stable performance measurement culture has yet to emerge and systematically utilize performance measurement reports for purposes of facilitating change. Although there is a growing recognition that measures by themselves will not be able to spur improvement, it is yet to be seen to what extent these performance reports can speak compellingly to policymakers, primary healthcare providers and managers to serve as catalysts to a major leap forward in overall quality improvement.
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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 | 0.016 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.028 | 0.020 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.046 | 0.048 |
| Insufficient payload (model declined to judge) | 0.006 | 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".