Bibliographic record
Abstract
All developed countries with universal healthcare systems provide universal coverage for prescription drugs – except Canada. Instead, Canadian provinces allocate limited public subsidies for prescriptions drugs, leaving the majority of costs to be financed out-of-pocket and through private insurance. We review three of the main approaches to provincial pharmacare policy – exemplified by British Columbia, Ontario, and Quebec – and compare them with policies in other countries. We find that Canadian models for prescription drug financing have major shortcomings. All provincial systems involve considerable patient charges and multiple payers that are not responsible for financing patients’ medical and hospital care. The costs borne by patients are known to reduce the use of medicines that might otherwise improve patient health and reduce costs elsewhere in the healthcare system. And the involvement of multiple payers adds administrative costs, diminishes purchasing power and creates funding silos that limit the potential for healthcare managers and providers to consider the full benefits and opportunity costs of prescription drugs as an input into the broader healthcare system. The performance of countries with comparable healthcare systems shows that integrating pharmaceuticals into the healthcare system by covering medically necessary prescription drugs at little or no cost to patients would result in improved performance on all key pharmacare policy goals. Countries with such coverage achieve better access to medicines, and greater financial protection for the ill, at significantly lower total cost than any Canadian province achieves. In this Commentary, we suggest that provinces expand public pharmacare programs to all segments of the population with a specific focus on promoting access to medicines of proven value-for-money in our healthcare system. Though the immediate effect of this would be an increase in government spending, this would, over time, be more than offset by savings to patients, employers and individuals who purchase stand-alone private drug coverage.
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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 | 0.015 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.016 | 0.014 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.028 | 0.028 |
| Insufficient payload (model declined to judge) | 0.006 | 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".