Awareness of the Food and Drug Administration's Bad Ad Program and Education Regarding Pharmaceutical Advertising: A National Survey of Prescribers in Ambulatory Care Settings
Bibliographic record
Abstract
The U.S. Food and Drug Administration's Bad Ad program educates health care professionals about false or misleading advertising and marketing and provides a pathway to report suspect materials. To assess familiarity with this program and the extent of training about pharmaceutical marketing, a sample of 2,008 health care professionals, weighted to be nationally representative, responded to an online survey. Approximately equal numbers of primary care physicians, specialists, physician assistants, and nurse practitioners answered questions concerning Bad Ad program awareness and its usefulness, as well as their likelihood of reporting false or misleading advertising, confidence in identifying such advertising, and training about pharmaceutical marketing. Results showed that fewer than a quarter reported any awareness of the Bad Ad program. Nonetheless, a substantial percentage (43%) thought it seemed useful and 50% reported being at least somewhat likely to report false or misleading advertising in the future. Nurse practitioners and physician assistants expressed more openness to the program and reported receiving more training about pharmaceutical marketing. Bad Ad program awareness is low, but opportunity exists to solicit assistance from health care professionals and to help health care professionals recognize false and misleading advertising. Nurse practitioners and physician assistants are perhaps the most likely contributors to the program.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".