Experimenting with Governance: Alberta’s Strategic Clinical Networks
Bibliographic record
Abstract
Alberta is undertaking a bold and somewhat risky step overhauling its health system governance to build higher performance in quality, safety and improved health outcomes for Albertans. On the heels of having re-established a single province-wide health authority (Alberta Health Services [AHS]), provincial health system decision makers have moved to establish province-wide Strategic Clinical Networks™ (SCNs). Sixteen SCNs have been implemented, and all are constituted as teams of healthcare professionals, researchers, government stakeholders, patients and families seeking to improve delivery of healthcare across the province. SCNs were developed in part as a strategy for strengthening clinical engagement to achieve a broad range of healthcare delivery benefits including improvement of clinical care processes and reduced variations in practice, better access to care and improved patient outcomes across the province. Here, we examine the rationale and potential of this governance intervention, while also considering some of the fundamental questions around their potential impact and the ultimate need for multidimensional assessment.
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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 | 0.043 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.017 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".