THE EVIDENTIARY BURDEN FOR OVERTURNING GOVERNMENT'S CHOICE OF REGULATORY INSTRUMENT: THE CASE OF DIRECT-TO-CONSUMER ADVERTISING OF PRESCRIPTION DRUGS
Bibliographic record
Abstract
This article explores Michael Trebilcock's claim that the federal government's present restrictions on direct-to-consumer advertising (dtca) of prescription drugs should not withstand a Charter challenge and his argument that a less intrusive, more nuanced regulatory regime could be implemented. The author explores the government's challenges in mounting a s. 1 defence, analysing the role and limitations of social-science evidence and recognizing that both inherent methodological difficulties and the manner in which health services researchers frame their approach to policy questions are such that there may never be sufficiently robust evidence of competing policy alternatives for the government to use in a s. 1 challenge. The article then goes on to review the appropriate evidentiary hurdles the government should be required to satisfy to justify this kind of policy in the face of a constitutional challenge and raises the question of the courts’ competence to assess the policy ramifications of choosing to take a more stringent approach to review. The policy approaches to dtca in other countries are explored to demonstrate that although alternative regulatory regimes exist in theory, the reality is that they are not enforced, and as such are not real alternatives to the current regime. The author explores what evidence is available regarding the advantages and disadvantages of dtca and concludes that the latter outweigh the former, that the prospect of more nuanced regulations are theoretical only, and that Canada should maintain its present regulatory restrictions.
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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 | 0.036 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.054 |
| Scholarly communication | 0.022 | 0.010 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.033 | 0.021 |
| 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".