Pharmaceutical industry promotional activities on social media: a scoping review
Bibliographic record
Abstract
Abstract Objectives The rise of social media has broadened the reach and impact of pharmaceutical promotion across countries. This scoping review synthesizes available literature on the nature, extent, and impacts of such promotion, with a particular focus on public health implications. Methods Using a systematic strategy, we searched six multidisciplinary scholarly databases for empirical studies, both peer-reviewed and grey, published since 2004, which had collected data on pharmaceutical promotion via social media. Data were synthesized qualitatively into outcome domains. Key findings We included 45 studies, primarily conducted in the USA (20/45, 44%) and multi-nationally (15/45, 33%), and published after 2013 (40/45, 89%). Studies used content analyses, surveys, and interviews to measure pharmaceutical industry presence or impacts on the following indicators: social media, social media strategy, consumer reach and engagement, health information quality, ethical and regulatory guideline adherence, and consumer attitudes and behaviours. Taken together, these studies indicate a gradual increase in industry use of social media, notably including the development of novel consumer engagement strategies, such as targeted promotion and influencer sponsorship. Studies also showed that, in some cases, health information provided on social media is of low quality, ethically and legally questionable, and potentially harmful to public health. Conclusions Appreciating the regulatory and reputational risks of consumer engagement on social media, the pharmaceutical industry has gradually increased promotional activities on social media since its inception. Evidence of harmful content and promotional activities that have become more covert and targeted suggests the need for regulatory development.
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.
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 | 0.022 | 0.097 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.038 | 0.027 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".