Bibliographic record
Abstract
BACKGROUND: Each province in Canada independently assesses drugs for their reimbursement eligibility. Publicly funded access to specific drugs is therefore dependent on province of residence. OBJECTIVE: Evaluate the variability of access and its determinants for publicly available prescription drugs across Canada, and discuss the feasibility of implementing a national plan. METHODS: For a sample of 58 drugs receiving Health Protection Branch approval in Canada between 01/01/1996 and 12/31/1997, all provinces were surveyed about their formulary inclusion/exclusion decision. Kappa values were estimated to measure concordance between provincial coverage decisions. Logistic analysis using Generalized Estimating Equations was used to assess the impact of key features of provincial plans on the decision. RESULTS: Among the 58 drugs, 5 (9%) were included in all 10 and 14 (24%) by at least 8 provincial formularies. None were excluded by all the provinces. Concordance rates among provinces were low (overall kappa-like statistic = 0.20 and range of pairwise kappa = -0.11 to 0.64). Logistic regression showed that therapeutic category, price ratio to comparator, the integration of public with private coverage, and the existence of ability-to-pay criteria were significant determinants of the inclusion decision. CONCLUSIONS: Findings show that public access to the same prescription medications differs widely across provinces. If Canada were to adopt a "National" plan without disrupting current individual prescriptions, all currently funded drugs in each province would have to be "grandfathered" and included in the new National formulary. Such an all-inclusive list would also make such a plan unaffordable.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.047 | 0.009 |
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".