Multinational Pharmaceutical Companies Shortchange Canada in Research and Development Investments: Is It Time to Pursue Other Options?
Bibliographic record
Abstract
In 1987, the government passed legislation to protect brand-name pharmaceutical firms against competition from generic drug brands in exchange for economic investment in Canadian pharmaceutical research and development (R&D).Since 2002, brand-name pharmaceutical companies' R&D investments have fallen short of their commitment, while Canadians now pay the fourth highest drug prices of all the Organisation for Economic Co-operation and Development member countries.In this article, we examine the degree to which brand-name pharmaceutical companies have fallen short of their promises, discuss whether a patent policy is the best strategy to secure Canadian pharmaceutical R&D funding and propose practical alternatives to this arrangement. RésuméEn 1987, le gouvernement adoptait une loi pour protéger les entreprises pharmaceutiques de médicaments de marque contre la concurrence des médicaments génériques, en échange d'investissements économiques dans la recherche et le développement (R et D) pharmaceutiques au Canada.Depuis 2002, les investissements en R et D effectués par les sociétés pharmaceutiques de médicaments de marque ont été inférieurs à leurs engagements, alors que le Canada figure en quatrième position des prix les plus élevés pour les médicaments parmi les pays membres de l'Organisation de coopération et de développement économiques.Dans cet article, nous examinons dans quelle mesure les sociétés pharmaceutiques de médicaments de marque ont brisé leurs promesses.Nous nous demandons aussi si une politique sur les brevets constitue la meilleure stratégie pour garantir le financement canadien dans la R et D pharmaceutique et nous proposons des alternatives pratiques à cet arrangement.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".