Alberta’s Strategic Clinical Networks: A roadmap for the future
Bibliographic record
Abstract
Clinical networks are groups of clinicians, patients, operational leaders, and other stakeholders who work together to solve health challenges, translate evidence into practice, and improve health outcomes and clinical care. Networks enable health, community, and academic partners to align their efforts, address priority issues, and advance quality improvements, health innovation, and transformational change on a local and system-wide scale. Clinical networks have existed in some countries for nearly 20 years. Alberta first implemented clinical networks in 2012 in specific areas of health. There are now 16 Strategic Clinical Networks (SCNs) in Alberta, embedded within a province-wide health system. The SCNs have developed an action plan that builds on their experience and identifies common areas of focus. This article describes the SCNs, their impact to date, and the objectives, areas of focus, and processes Alberta's SCNs will use to improve health outcomes and health system performance over the next 5 years.
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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 | 0.033 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.013 | 0.010 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".