The Effect of Medical Marijuana Legalization on Pharmaceutical Payments to Physicians
Bibliographic record
Abstract
Although cannabis is federally prohibited, a majority of U.S. states have implemented medical cannabis laws (MCLs). As more individuals consider the drug for medical treatment, they potentially substitute away from prescription drugs. Therefore, an MCL signals competitor entry. This paper exploits geographic and temporal variation in MCLs to examine the strategic response in direct-to-physician marketing by pharmaceutical firms as cannabis enters the market. We use office detailing records from 2014-2018 aggregated to the county level and find detailing increases by 7% in the quarter an MCL is proposed. The increase is temporary, however, and attenuates after MCL approval. We then incorporate physician-level cannabis recommendation data from Florida and find opioid detailing to cannabis-friendly doctors declines following MCL enactment. Although we find weak evidence of a similar decline in our primary analysis, the effects are muted at the aggregate level by the small percent of doctors that recommend cannabis.
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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 | 0.001 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".