Healing Healthcare in Canada: A Shared Agenda for Healthcare Quality and Sustainability
Bibliographic record
Abstract
Sullivan et al. make a compelling argument that a "coalition of the willing" must seize the nettle and create a national agenda and the capacity for quality leadership in Canadian healthcare. While there is reason to believe that Canada could benefit from such an agenda, there is also evidence that, if done incorrectly, such an agenda could be expensive and counterproductive. To increase the likelihood that a national quality agenda will contribute to the creation of a sustainable and effective healthcare system, it will be important to understand potential pitfalls and to incorporate approaches that have enabled leading organizations to achieve success. It will be key to create a shared vision of healthcare that focuses on the health needs of our population and engages stakeholders broadly.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.030 | 0.017 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.073 | 0.081 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".