Patient Influencers: The Next Frontier in Direct-to-Consumer Pharmaceutical Marketing
Bibliographic record
Abstract
Social media influencers are becoming an increasingly popular strategic communication tactic used across industry verticals, including entertainment, fashion, and beauty, to engage directly with consumers. Pharmaceutical companies have also recently entered the social media marketing arena and-within the bounds of governmental regulations-have found ways to build relationships directly with patients using covert persuasion tactics like partnering with social media influencers. Due to consumers' negative perceptions of pharmaceutical companies, it makes sense that new marketing tactics are being used to establish and improve relationships with consumers. Previous research well documents the ethical dilemmas of direct-to-consumer advertising, and there is recent burgeoning literature on online covert marketing tactics. The academic and medical literature, however, is behind in regard to social media influencers used in health and medicine. This paper highlights and defines terms used in industry practice, and also calls for more investigation and sets forward a research agenda. As consumers spend more time online and patients continue to consult social media for health information, it is important that this new marketing trend does not go unnoticed.
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 | 0.021 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".