A Case for Pharmacare in Canada: Lessons Learned from the UK
Bibliographic record
Abstract
In 2014, total drug expenditure in Canada was $33.9 billion, of which a significant proportion (85%) was spent on prescribed drugs.1 Prescription drugs have become an integral component of modern medicine as they can help treat diseases and greatly improve quality of life.2 However, unlike most other countries with a universal health system, Canada does not universally cover prescription drugs.2 While some level of public drug coverage is provided by all provinces and territories in Canada, 58% of prescription drug costs are covered by private health insurance plans or out-of-pocket payments.2 As a result, there is growing interest among the Canadian public to implement a public drug insurance program, often termed “Pharmacare,” to improve access to prescription drugs.2 The United Kingdom (UK) has had a public financing system for health coverage in place, including prescription drugs, for decades. This article explores what Canada can learn from the UK’s successful universal drug coverage program.
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.015 | 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".