Regulating Direct-to-Consumer Advertising of Prescription Drugs in the Digital Age
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The increasing prominence of new Internet and social media technologies and their growing importance as a source of health information are pushing the pharmaceutical industry towards digital channels. This paper explores the potential impacts of the pharmaceutical industry’s increasing interest in online marketing and considers how the existing regulatory framework in Canada translates into the social media sphere. Direct-to-consumer advertising (DTCA) of prescription drugs is prohibited in Canada, as it is in most industrialized countries. Although Health Canada has reaffirmed that the existing DTCA regulations apply to new Internet and social media technologies, new dynamics such as user-generated content, consumer propagation, and targeted marketing make applying the existing regulations an uncertain process. Moreover, certain problems often associated with DTCA may be exacerbated in the social media context. Finally, there is skepticism around whether government regulators have the resources or political will to effectively monitor new digital media. As such, this paper considers not only the role of direct government regulation in monitoring and enforcing the regulation of DTCA, but also the role of third party oversight and industry self-regulation—both of which may play an important role in filling the gaps in the regulation of the Internet and social media.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it