The impact of Canadian direct-to-consumer prescription medicine advertising on prescription volume
Bibliographic record
Abstract
Canadian pharmaceutical companies are increasingly using direct-to-consumer advertising (DTCA) of prescription medications in an attempt to stimulate sales. While published literature on the impact of DTCA is limited, one study conducted in the United States by Basara (1996) suggested that DTCA has a positive impact on new prescription volume and thus sales. This study investigates the reliability of findings of Basara (1996) and extends them using a Canadian prescription medication that was advertised directly to consumers. IMS Health Canada new prescription data was obtained for a prescription medication. The same promotional-response modeling technique known as intervention time-series analysis that Basara (1996) used, was used to investigate the potential for DTCA to impact new prescription volume for this medication. Results suggest that DTCA did not have an identifiable impact on new prescription volume for this product. Findings suggest that caution should be used when interpreting Basara's (1996) findings in the Canadian context. Specifically, the impact of DTCA appears to be dependent on product, market, and advertisement related factors. Research implications, limitations, and directions for future research are discussed.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".