Improving Cardiovascular Outcomes in Nova Scotia (ICONS): A Successful Public-Private Partnership in Primary Healthcare
Bibliographic record
Abstract
Broadly defined, disease, or health management, is a focused application of resources to improve patient outcomes; its premise: things can be better. In particular, the gap between what best care could be, and what usual care is, can be reduced and, consequently, care and outcomes can be improved. This paper reviews the evolution of the partnership/measurement paradigm of disease management and considers its value in sustaining Canadian healthcare. Lessons from ICONS (Improving Cardiovascular Outcomes in Nova Scotia), a major public-private health partnership of physicians, nurses, pharmacists, patients and their advocacy groups, government and industry, are highlighted. Launched in 1997, ICONS' proof-of-concept phase ended in 2002. Due to its positive impact on the cardiovascular health of the population and its integrated and accountable administrative processes, ICONS became an operational program of the Nova Scotia Department of Health. This successful community-based partnership represents a major achievement in organizational behaviour in the arena of primary healthcare. It supports optimal care as evidence-based and seamless, recognizing the patient as the nucleus. It should be considered for other disease states and constituencies where the goals are closing care gaps and delivering the best health to the most people at the best cost.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".