Regulatory authority and clinical acceptability: Physicians' responses to regulatory drug safety warnings
Bibliographic record
Abstract
AIMS: Medicines regulators issue post-market safety warnings to advise of newly uncovered risks, but with mixed impacts. We aimed to identify factors influencing the use of regulatory warnings by primary care and specialist physicians in the US and Australia. METHODS: Semi-structured qualitative interviews were carried out with 40 primary care physicians, endocrinologists and other generalist specialists in Boston (USA) and Australia. Coding and analysis were performed inductively and iteratively to identify and examine key factors. Analysis centred around four areas: physicians' awareness of drug safety information, preferred information sources, opinion-forming and sharing of information with patients. RESULTS: Uncertainty, trust and clinical authority emerged as factors influencing use of advisories. Although regulators were trusted as authoritative institutions, they appeared to lack clinical authority, and physicians validated regulatory information against other trusted sources including evidence, expert opinion and experience. Specialists became aware of drug safety issues through specialised literature, using evidence and clinical consensus to form opinions. Primary care physicians, fielding high volumes of information, relied on convenient, accessible information sources including the media and the "clinical grapevine" for awareness, and on clinical colleagues, specialists and experience for interpretation. Communicating risk to patients was complicated by uncertainty; physicians tailored information to patients' health literacy and information needs. US physicians were more aware of their national regulator's post-market safety role than Australian physicians of theirs. CONCLUSION: Drug safety warnings may not be optimally received or used. Regulators should consider strategies that increase trust, clinical relevance and accessibility, and address physicians' needs in communicating risk to patients.
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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 | 0.038 | 0.172 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".