Medicare’s Evolution: National Pharmacare and Shared Leadership
Bibliographic record
Abstract
Repeated Health Care in Canada (HCIC) surveys over the past two decades have consistently reported that the adult public and clinical and administrative health professionals consider medicare to be successful in terms of quality of care, despite a growing concern that timely access to care remains challenging. These key stakeholders have also recently signalled that major change strategies are likely necessary for continuing success. In the 2018 survey, both the public and professionals ranked highest the creation of a national comprehensive pharmacare plan, entirely funded by the federal government, or with federal funding for those not currently covered by existing pharmaceutical plans. The majority of the public and health professionals in 2018 were also remarkably concordant regarding preferred leadership for designing, instituting and managing a national pharmacare program. The public's priority, supported by 50% of the adult population, was shared leadership involving governments, medical academia and the pharmaceutical/biotech industries, followed by government leadership at 33%. Among professionals, preference for shared leadership averaged 60% and governmental leadership averaged 33%. Based on these data, restriction of pharmacare's leadership exclusively to any single stakeholder raises concern of a critical lack of support for success. A coalition of governments, research hospitals/health authorities and the pharmaceutical/biotech industry - the highest-ranked candidates as potential leaders - would likely provide the best chance to garner the majority of public support and enhance the chances of success in the short and long terms. In summary, the addition of universal pharmacare to medicare's existing healthcare portfolios is an attractive strategy to advance Canadian healthcare and outcomes. The federal government has taken the initial step. Recruitment of additional leaders sharing aspiration, inspiration and experience to optimize pharmacare's development and measure its outcomes is needed. Things can be better.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".