Measuring the Informative and Persuasive Roles of Detailing on Prescribing Decisions Andrew Ching ∗
Bibliographic record
Abstract
In the pharmaceutical industry, measuring the importance of informative and persuasive roles of detailing is crucial for both drug manufacturers and policy makers. However, little progress has been made in disentangling between informative and persuasive roles of detailing. In this paper, we provide a new identification strategy to separately identify these two roles. Our key identification assumption is that the informative component of detailing is chemical specific while the persuasive component is brand specific. Our strategy is to focus on markets where some drug manufacturers engage in a co-marketing agreement. Under a co-marketing agreement, two companies market the same chemical under two different brand-names. With our identification assumption, the relative market share of these two brands, together with their individual detailing efforts, would allow us to measure the persuasive component of detailing. The total market share of the chemical, and the sum of the brand-specific detailing efforts for this chemical, would allow us to measure the informative component of detailing. To demonstrate our identification strategy, we estimate two structural models of detailing and prescribing decisions that have been used in the literature, using monthly product level data on sales, prices, and detailing minutes for ACE-inhibitor with diuretic in Canada. This market has three brandname
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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 | 0.011 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".