Awareness and Perceptions of Vermont’s Prescribed Product Gift Ban and Disclosure Law by Prescribers and Pharmacists
Bibliographic record
Abstract
PURPOSE: Vermont law strictly regulates the interactions between pharmaceutical manufacturers and health care providers, including gifts, meals, and medication samples. The purpose of this study was to describe providers' awareness and perceptions of current requirements. METHODS: An online survey was completed by Vermont providers, including prescribers and pharmacists. The survey asked providers about their awareness of 15 different legal requirements and about their level of agreement with these requirements. RESULTS: Four hundred and eleven providers completed the survey (61% male, mean age 52 years, and 71% physicians). Awareness of the 15 requirements ranged from 28.4% to 93.8%. Most providers agreed or had no strong opinions. Responses at significance levels of P < .001 were noted in 8 of 15 requirements when perceptions were stratified by providers who had any interactions with pharmaceutical representatives in the past year (N = 227, 55.4%) versus providers who reported no interactions (N = 183; 44.6%). CONCLUSIONS: A high proportion of Vermont providers are unaware of the current law. Most agreed or had no strong opinions about the requirements; however, at least a quarter disagreed with banning small gifts and meals. Having any interaction with pharmaceutical representatives changed how providers perceived the requirements. These data may be useful for other states considering similar laws.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".