Pharmaceutical Industry Funding to Patient-Advocacy Organizations: A Cross-National Comparison of Disclosure Codes and Regulation
Bibliographic record
Abstract
Transparency has become one of the primary themes in health care reform efforts in the United States and across the world. In the face of exorbitant drug prices, high levels of patient cost-sharing, and pharmaceutical expenditures that consume a growing proportion of public sector budgets, much attention has been drawn to the pharmaceutical industry. Congressional investigations, academic publications, and news articles have endeavored to reveal the extent of drug and device industry influence on health care actors. In response, several nations, including the United States, have passed legislation mandating disclosure of drug company payments to physicians. In the United States, there are currently no legal requirements for disclosure of pharmaceutical industry sponsorship to patient-advocacy organizations by either party to the transaction. An ongoing concern is that drug industry payments could interfere with the objectivity of patient-advocacy groups and may induce them to take public positions favorable to the drug industry but at odds with the interests of patients. This article provides a comparative analysis of industry codes of practice and regulation that govern relationships between pharmaceutical companies and patient-advocacy organizations in the United States, the United Kingdom, Germany, France, Australia and Canada, with an emphasis on disclosure policies for industry sponsorship. The article draws upon the practices of other nations and the Physician Payments Sunshine Act to make a case for an expansion of the Sunshine Act to patient-advocacy groups.
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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 | 0.036 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".