The 2018 decision to establish an Advisory Council on adding pharmaceuticals to universal health coverage in Canada
Bibliographic record
Abstract
Canada is the only Universal Health Insurance country in the OECD without universal insurance for outpatient prescription drugs, a situation generally perceived as unfair and inefficient. In June 2018, the federal government launched an Advisory Council on the Implementation of National Pharmacare, to report in 2019 on the best strategy to implement a national Pharmacare program that would provide all Canadians access to affordable outpatient prescription drugs. The Council was asked to consider three options: a universal public plan for all Canadians; a public catastrophic insurance plan that would kick off once spending on prescription drugs reaches a given threshold; and a more modest patching of existing gaps, providing coverage to those who are not eligible to any form of insurance. Beyond the relative consensus around the ideas that gaps in coverage should be filled to make drugs affordable to all, and that the costs of drugs are too high in Canada, the Council faces the challenge of addressing three underlying issues: 1) what amount of income redistribution will result from each of the three options; 2) how much savings would the implementation of a single payer generate? 3) what role restricting a national formulary would play in achieving those savings, and what would be the political consequences of narrowing the formulary?
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.044 | 0.025 |
| Insufficient payload (model declined to judge) | 0.013 | 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".