Interprovincial Variation in Access to Publicly Funded Pharmaceuticals
Bibliographic record
Abstract
Introduction: Government-funded drug programs provide prescription medications for many Canadian residents, including senior citizens and social assistance recipients. Pharmaceuticals available for beneficiaries are typically listed in federal, provincial, and territorial formularies. We analyzed six Canadian drug formularies (for the provinces of Alberta, British Columbia, Manitoba, Nova Scotia, Ontario, and Quebec) and report the extent of interprovincial variation in Canadians' access to publicly funded pharmaceuticals at the chemical subgroup (CSG) level of the World Health Organization Anatomical Therapeutic Chemical (WHO ATC) classification system. Methods: A database profiling provincial formulary listings of CSGs was compiled to enable a cross-sectional analysis of drug benefits at a clinically meaningful level. The comprehensiveness of provincial drug reimbursement plans was evaluated in a quantitative comparison of CSGs. Therapeutic distribution of CSGs within anatomical main groups of the WHO ATC classification system was also investigated. Interprovincial formulary agreement of CSG listings (full, restricted, or not listed) was determined on the basis of kappa coefficients. Results: British Columbia and Nova Scotia provided residents with access to the greatest number of full-listing CSGs, 336 each. Manitoba had the fewest full-listing CSGs, 268. Kappa coefficients, representing agreement in provincial listing decisions, ranged from 0.23 (between British Columbia and Quebec) to 0.45 (between Alberta and Manitoba). All of these coefficients represent a fair-to-poor level of interprovincial agreement in CSG listing status. Conclusion: A large degree of variation is present in Canadian provincial drug formularies, even at the CSG level. This reflects differences in provincial listing decisions and has therapeutic implications for patients, in that there is differential access to entire categories of drug products across provincial drug plans.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".