Offering Pharmaceutical Samples: The Role of Physician Learning and Patient Payment Ability
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
Physicians may learn about prescription drug effectiveness directly from the firm via detailing or from patient experience. Patient-mediated learning is aided by the use of free drug samples. The effective use of samples is hampered by a lack of understanding of its exact return on investment implications. We seek to fill this gap by incorporating the physician's sample allocation behavior in the firm's decision making. We uncover the following implications for firms as well as policy makers. First, we find that the optimal sampling level for a drug category is a nonmonotonic function of patient payment ability and the price of the drug. Second, an increase in the cost of samples can lead to an increase in sampling and a decrease in detailing when the physician's propensity to provide sample subsidies is high. Third, when future market growth is expected to be high (early stage product life cycle and/or chronic drugs) and sampling efficiency is low, the use of sampling is profitable for the firm but leads to lower market coverage than when sampling is disallowed.
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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 | 0.003 | 0.001 |
| 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 | 0.000 |
| 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