Transparency and the Food and Drug Administration—A Quantitative Study
Bibliographic record
Abstract
In Europe and North America, there is increasing political pressure being put on health regulatory agencies to become more transparent. To date, however, there has been little academic evaluation--let alone analysis--of these transparency initiatives from a risk communication perspective. This review examines whether the U.S. Food and Drug Administration's Adverse Event Reporting System quarterly signal postings, put in place after the passage of the Food and Drug Administration Amendments Act 2007, will assist patients and doctors in their decision-making processes, on the basis of results of a quantitative Internet survey of 433 physicians and 1,000 American adults. The results indicate that there is significant disagreement between physicians and the public about when medical safety issues should be communicated in the first place, with physicians opposed to early signal postings while the public in general is in favor. In addition the findings show that if the public were to find their drugs listed on the Adverse Event Reporting System signals web postings, more than a quarter would stop taking their medicine. Going forward, the Food and Drug Administration needs to work to a greater degree with social scientists in developing scientific-based communication strategies, rather than developing transparency initiatives on the basis of stakeholder consultations.
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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 | 0.023 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".