Detailing vs. Direct-to-Consumer Advertising in the Prescription Pharmaceutical Industry
Bibliographic record
Abstract
The pharmaceutical industry has always used sales representatives to target physicians (detailing), who are a key link in sales and market share for prescription pharmaceuticals. Since August of 1997, when the Food and Drug Administration eased the restrictions on direct-to-consumer advertising (DTCA), there has been a dramatic increase in the use of DTCA by pharmaceutical firms to target end customers (patients). DTCA seems to have two different effects on pharmaceutical markets. The first is to inform patients about the availability of drugs for some ailments, thus expanding the market (constructive). The second is to persuade patients to talk about specific brands when they meet physicians, with the objective of influencing market share (combative). We consider both effects of DTCA in the presence of a detailing program in a competitive environment. We incorporate the dynamics of physician-patient interaction in a game-theoretic model where firms decide on the form of DTCA to adopt (constructive or combative) and then compete in the marketplace by choosing detailing and DTCA levels. We answer four questions: What is the impact of adopting DTCA on competitive intensity? How do optimal detailing levels for a firm change with the adoption of DTCA? How should the DTCA strategy for a firm vary depending on whether it is stronger or weaker in its degree of influence in the physician's office? Finally, under what conditions would competing firms voluntarily decide to pursue constructive DTCA?
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".