Canada's Future Healthcare: Can It Be Better? Will It Be Better?
Bibliographic record
Abstract
Thought leaders envisage high-performing partnerships of engaged community practitioners, informed patients and non-professional caregivers collaborating continuously, and efficiently, to improve care and outcomes for whole patient populations. These primary care health social networks would be facilitated by needs-based training, meaningful measurements, sustained funding, effective leadership and integration with available resources and processes. Broadly voiced opinion supports such integrated, community-focused partnership and data-driven healthcare models, and a province-wide implementation of the model for acute and chronic cardiac diseases in Nova Scotia has conclusively demonstrated sustained improvements in clinical and economic outcomes. A reasonable hypothesis, then, is that such strategies will be rapidly adopted to effectively manage the primary care of our increasingly aged populations, with their large and recalcitrant gaps between usual and best care. However, there are impediments to widespread adoption in the short term, not the least being disparities in various key stakeholders' level of preference, commitment, resolve and clout in making the necessary decisions to adopt and sustain the strategies. Thus, while we know things can be better in Canadian healthcare, the answers to, will they? and, when? remain less certain.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.040 | 0.041 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".