Program Design and Long-Run Costs of a National Catastrophic Drug Insurance Plan
Bibliographic record
Abstract
There are strong arguments that national catastrophic drug insurance should be established in Canada with assistance from the federal government.The author of this paper projects the long-run program costs for two proposals for such a program: that of the Kirby Committee and that of the Romanow Commission.He concludes that both the annual program costs to the federal government and the share of the federal government on total prescription drug expenditures in Canada would increase dramatically under either program.Although the Kirby-style program requires less initial expenditure by the federal government than does the Romanow-style program, because of their different designs, over time the Kirby-style program would become more expensive.Moreover, the Kirby-style program would be more sensitive to the [e140] HEALTHCARE POLICY Vol.3 No.4, 2008 Hai Zhong growth rate of prescription drug expenditure.The choices relating to the program threshold and federal cost-sharing rate have far-reaching influences on the long-run costs to the federal government. RésuméIl y a de nombreux arguments en faveur de l' établissement, au Canada, d'un régime national d' assurance médicaments pour faire face aux situations où les coûts des médicaments d' ordonnance sont exorbitants; régime qui se ferait avec la participation du gouvernement fédéral.L'auteur de l' article projette les coûts à long terme d'un tel programme en fonction de deux propositions en ce sens : celle du rapport Kirby et celle du rapport Romanow.L'auteur conclut que les coûts annuels du programme pour le gouvernement fédéral et que la part du gouvernement au total des dépenses pour les médicaments d' ordonnance au Canada augmenteraient considérablement dans le cas des deux programmes proposés.Bien que le programme de Kirby nécessite moins de dépenses initiales de la part du gouvernement fédéral que le programme de Romanow, en raison des différences de conception, à la longue le programme de Kirby serait plus coûteux.De plus, le programme de Kirby serait plus sensible à la croissance des taux de dépense pour les médicaments d' ordonnance.Les choix quant au seuil-limite et au taux de partage des frais du gouvernement fédéral ont une profonde influence sur les dépenses à long terme pour le gouvernement.
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".