Unethical pharmaceutical marketing: a common problem requiring collective responsibility
Bibliographic record
Abstract
Shai Mulinari and Piotr Ozieranski argue healthcare professionals and organisations should respond more forcefully to unethical marketing and support stronger regulatory action The marketing practices used by pharmaceutical companies have been a longstanding concern,12 with controversial techniques including the use of medical opinion leaders and third parties such as patient advocacy groups. In many jurisdictions, including Europe,3 Japan,4 Canada,5 and Australia,6 marketing by pharmaceutical companies is largely regulated by the industry itself, based on codes of practice drawn up by national industry trade groups. The UK has one of the most advanced and extensively studied self-regulatory systems in Europe78910 and globally (box 1).34 Box 1 ### UK’s pharmaceutical industry self-regulation Oversight of prescription drug marketing in the UK is delegated by the medicines and medical device regulator, the Medicines and Healthcare Products Regulatory Agency (MHRA), to the industry trade group, the Association of the British Pharmaceutical Industry (ABPI), and its self-regulatory body, the Prescription Medicines Code of Practice Authority (PMCPA).7 The PMCPA’s jurisdiction is accepted by virtually all drug companies operating in the UK, including about 70 ABPI members and over 60 non-members that follow the ABPI code voluntarily.11 #### PMCPA sanctions Companies found to be in breach of the ABPI code are required to pay “administrative charges” to contribute to the costs of processing complaints.11 These charges, which are explicitly defined as not being fines, are typically £3500 but increase to £12 000 if an appeal against a ruling is unsuccessful. In cases of more serious wrongdoing, the PMCPA can publicly reprimand a company or require it to issue a corrective statement. For both sanctions the company pays the cost of advertising these in medical ( The BMJ ), pharmaceutical ( Pharmaceutical Journal ), and nursing ( Nursing Standard ) publications. The PMCPA can also request compulsory audit of a … RETURN TO TEXT
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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 | 0.093 | 0.142 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.018 | 0.093 |
| Scholarly communication | 0.031 | 0.040 |
| Open science | 0.006 | 0.028 |
| Research integrity | 0.036 | 0.042 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".