Strategic Clinical Networks: Alberta's Response to Triple Aim.
Bibliographic record
Abstract
Verma and Bhatia make a compelling case for the Triple Aim to promote health system innovation and sustainability. We concur. Moreover, the authors offer a useful categorization of policies and actions to advance the Triple Aim under the "classic functions" of financing, stewardship and resource generation (Verma and Bhatia 2016). The argument is tendered that provincial governments should embrace the Triple Aim in the absence of federal government leadership, noting that, by international standards, we are at best mediocre and, more realistically, fighting for the bottom in comparative, annual cross-country surveys. Ignoring federal government participation in Medicare and resorting solely to provincial leadership seems to make sense for the purposes of this discourse; but, it makes no sense at all if we are attempting to achieve high performance in Canada's non-system (Canada Health Action: Building on the Legacy 1997; Commission on the Future of Health Care in Canada 2002; Lewis 2015). As for enlisting provincial governments, we heartily agree. A great deal can be accomplished by the Council of the Federation of Canadian Premiers. But, the entire basis for this philosophy and the reference paper itself assumes a top-down approach to policy and practice. That is what we are trying to change in Alberta and we next discuss. Bottom-up clinically led change, driven by measurement and evidence, has to meet with the top-down approach being presented and widely practiced. While true for each category of financing, stewardship and resource generation, in no place is this truer than what is described and included in "health system stewardship." This commentary draws from Verma and Bhatia (2016) and demonstrates how Alberta, through the use of Strategic Clinical Networks (SCNs), is responding to the Triple Aim. We offer three examples of provincially scaled innovations, each representing one or more arms of 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 | 0.041 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.032 | 0.020 |
| Scholarly communication | 0.020 | 0.006 |
| Open science | 0.007 | 0.021 |
| Research integrity | 0.046 | 0.037 |
| Insufficient payload (model declined to judge) | 0.011 | 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".