Medicines pricing and reimbursement in Canada
Bibliographic record
Abstract
Objective: overview of Canadian practices for regulating, financing, and funding prescription drugs. Canada provides universal health coverage for hospital and physician services but excludes universal insurance of prescription medicines. Public plans provide 42% of financing, while private drug insurance covers 35% of expenditures and over 60% of Canadians – mainly through their employer. Canada has relatively high out-of-pocket expenditure (19% of spending) and is currently the tenth largest pharmaceutical market, following Brazil. It is wrestling with inequitable coverage, low use of biosimilars, and affordability and sustainability issues driven by rare disease drugs. Both federal and provincial/territorial governments and their agencies have roles in setting policy and regulating drug prices and costs. These include the federal Patented Medicine Prices Review Board (PMPRB) which ensures prices of new patented drugs are not excessive; the pan-Canadian Pharmaceutical Alliance (pCPA) which negotiates lower patented, generic and biosimilar drug prices on behalf of member jurisdictions; and the Canadian Agency for Drugs and Technologies in Health (CADTH) which provides most public drug plans with robust health technology assessment (HTA), including clinical, economic and budget impact analyses of new drugs. Private drug insurers tend to follow government initiatives, including the use of HTA and confidential Product Listing Agreements. Conclusions: Pharmaceutical coverage in Canada is a “patchwork” of more than 100 public drug plans and 100,000 private insurance plans. As such, it creates gaps in coverage which result in inequitable access and high out-of-pocket drug expenses for some Canadians. Canada’s decentralized health system and the absence of universal drug insurance, among other factors, likely contribute to higher per capita drug expenditure relative to comparable nations that have broader, publicly-funded universal health insurance and more rigourous policy and program strategies.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".