Transforming Regions into High-Performing Health Systems Toward the <i>Triple Aim of Better Health, Better Care and Better Value for Canadians</i>
Bibliographic record
Abstract
A study on the impact of regionalization on the Triple Aim of Better Health, Better Care and Better Value across Canada in 2015 identified major findings including: (a) with regard to the Triple Aim, the Canadian situation is better than before but variable and partial, and Canada continues to underperform compared with other industrialized countries, especially in primary healthcare where it matters most; (b) provinces are converging toward a two-level health system (provincial/regional); (c) optimal size of regions is probably around 350,000-500,000 population; d) citizen and physician engagement remains weak. A realistic and attainable vision for high-performing regional health systems is presented together with a way forward, including seven areas for improvement: 1. Manage the integrated regionalized health systems as results-driven health programs; 2. Strengthen wellness promotion, public health and intersectoral action for health; 3. Ensure timely access to personalized primary healthcare/family health and to proximity services; 4. Involve physicians in clinical governance and leadership, and partner with them in accountability for results including the required changes in physician remuneration; 5. Engage citizens in shaping their own health destiny and their health system; 6. Strengthen health information systems, accelerate the deployment of electronic health records and ensure their interoperability with health information systems; 7. Foster a culture of excellence and continuous quality improvement. We propose a turning point for Canada, from Paradigm Freeze to Paradigm Shift: from hospital-centric episodic care toward evidence-informed population-based primary and community care with modern family health teams, ensuring integrated and coordinated care along the continuum, especially for high users. We suggest goals and targets for 2020 and time-bound federal/provincial/regional working groups toward reaching the identified goals and targets and placing Canada on a rapid path toward the Triple Aim.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".