Regulating Information or Allowing Deception? Pharmaceutical Sales Visits in Canada, France, and the United States
Bibliographic record
Abstract
Diverse legal and regulatory measures are used internationally to control the information provided during pharmaceutical sales visits. Little is known about the comparative effectiveness of these measures however. We analyzed the perceptions of regulators, pharmaceutical industry officials, health professionals, and consumer respondents concerning these approaches in Canada, France, and the United States using an empirical realist interests-based approach. Interviews focused on the aims and effectiveness of regulation, barriers and enablers to regulation and suggestions for improvement. An alignment was found in North America regulator and industry respondents' satisfaction with the status quo and their view that further intervention is unfeasible and unnecessary. Health professionals generally expressed a lack of confidence in the impact of regulations on sales visit information while consumer advocates voiced their disappointment in both regulators and health professionals for their failure to counteract the influence of pharmaceutical marketing. Regulator and industry respondents in France differed from their North American counterparts in their willingness to increase and diversify the scope of regulatory interventions. As the first international comparison of regulatory experiences in this sector, the findings highlight the universal need for more focused and inclusive discussions among groups about how to tailor regulations to achieve public health goals.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".