International Experience With Pharmaceutical Policy: Common Challenges and Lessons for Canada
Bibliographic record
Abstract
Pharmaceuticals are the focus of increased scrutiny by public insurers. Between 1985 and 1998, drug expenditure in Canada increased by 226% - approximately double the increase in total expenditure on health. Prescribed and non-prescribed drugs now comprise the second-largest share of health care expenditures after hospitals, surpassing physicians’ services. The National Forum on Health called for common strategies across the provinces, to manage pharmaceuticals from a health policy perspective. At the same time, the federal government and several provinces are interested in promoting pharmaceutical research and development (R&D), as part of the advancement of a knowledge-based economy. In the past, debates about pharmaceutical policy centred on the balancing of cost-containment and access to needed pharmaceuticals. The creation of an environment more conducive to attracting pharmaceutical R&D introduces additional tensions that will, no doubt, require concessions in current policies to manage pharmaceutical expenditures. In addition, a significant R&D investment will have predictable “downstream” effects on other sectors, such as academic research. In this study, we describe the experience of 7 Western industrialized countries in controlling pharmaceutical budgets while maintaining access to medically necessary prescription medications. In addition, we describe the potential impact of these policies on pharmaceutical R&D and the efforts of these countries to create a favourable climate for fostering R&D within their borders. We identify tensions that arise between health policy and industrial policy goals, and broad questions of directions and choices.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.015 |
| Science and technology studies | 0.027 | 0.017 |
| Scholarly communication | 0.026 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".