“There are ways … drug companies will get into DTC decisions”: How Australian drug and therapeutics committees address pharmaceutical industry influence
Bibliographic record
Abstract
AIMS: One tool for protecting quality use of medicines in hospitals is a drug and therapeutics committee (DTC) that oversees medicines availability. Pharmaceutical industry marketing to prescribers is associated with less appropriate prescribing and increased costs. There is little data on decision-making practices of DTCs so it is unknown whether or how they might be vulnerable to pharmaceutical industry influence. This project explores DTC decision-making with a focus on how pharmaceutical industry influence on access and use of medicines is identified and managed. METHODS: We used a qualitative methodology with individual interviews of 29 participants who were current or recent members of public hospital DTCs across New South Wales, Australia. Participants included medical, pharmacy and nursing staff and 1 citizen. Committees were linked to specific hospitals or regions, and some were affiliated with paediatric, neonatal, rural or mental health services. RESULTS: Drug committee processes for oversight of medicines in public hospitals are vulnerable to pharmaceutical industry influence at several points. Applications for formulary additions are sometimes initiated and completed by company representatives. Conflict of interest disclosures among applicants and committee members may be incomplete. In some institutions, medicines are available from pharmaceutical companies without committee review, including through free samples and industry-supported medicines access programmes. Participants noticed the presence and impact of pharmaceutical company marketing activities to local clinicians, resulting in increased prescriber demand for products. CONCLUSION: Improved DTC practices and review of hospital policies concerning pharmaceutical marketing activities might preserve the independence of evidence-based decision-making for safe, cost-effective prescribing.
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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 | 0.161 | 0.221 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.029 | 0.033 |
| Scholarly communication | 0.023 | 0.016 |
| Open science | 0.005 | 0.023 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.006 | 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".