Twelve years’ experience with direct-toconsumer advertising of prescription drugs in Canada: a cautionary tale
Bibliographic record
Abstract
Background: Direct-to-consumer advertising (DTCA) of prescription drugs is illegal in Canada as a health protection measure, but is permitted in the United States. However, in 2000, Canadian policy was changed to allow ‘reminder’ advertising of prescription drugs. This is a form of advertising that states the brand name without health claims. ‘Reminder’ advertising is prohibited in the US for drugs that have ‘black box ’ warnings of serious risks. This study examines spending on DTCA in Canada from 1995 to 2006, 12 years spanning this policy shift. We ask how annual per capita spending compares to that in the US, and whether drugs with Canadian or US regulatory safety warnings are advertised to the Canadian public in reminder advertising. Methodology/Principal Findings: Prescription drug advertising spending data were extracted from a data set on health sector spending in Canada obtained from a market research company, TNS Media Inc. Spending was adjusted for inflation and compared with US spending. Inflation-adjusted spending on branded DTCA in Canada grew from under CAD$2 million per year before 1999 to over $22 million in 2006. The major growth was in broadcast advertising, accounting for 83 % of spending in 2006. US annual per capita spending was on average 24 times Canadian levels. Celebrex (celecoxib), which has a US black box and was subject to three safety advisories in Canada, was the most heavily advertised drug on Canadian television in 2005 and 2006. Of 8 brands with.$500,000 spending, which together accounted for 59 % of branded DTCA in
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".