Going far together: Healthcare collaborations for innovation and improvement in Canada
Bibliographic record
Abstract
Healthcare in Canada, as elsewhere, must adapt in order to better meet the needs of the chronically ill. Such adaptations are happening locally, but healthcare decision- and policy-makers require channels and mechanisms for sharing project outcomes and spreading or scaling up successful approaches. Without formal mechanisms, there is a risk of losing the rich knowledge produced by improvement projects; of compromising the efficient use of healthcare resources; and of negatively impacting the further distribution of potential outcomes and impacts. This paper profiles three Canadian collaborations, supported by the Canadian Foundation for Healthcare Improvement (CFHI), which supports healthcare leaders in working together to develop, share, implement, and sustain evidence-informed and systems solutions. The collaborations are team based and particularly relevant to patient engagement and chronic disease care. They illustrate early lessons on how collaborative partnerships, with a shared vision and ownership, can co-address multiple components, conditions and communities, using evidence-based approaches and embedding performance measurement and evaluation. They also demonstrate the role organizations such as CFHI can play in facilitating a collaborative approach to accelerating healthcare improvement within and across organizations or systems.
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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 | 0.015 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.048 | 0.012 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".