Decoding disclosure: Comparing conflict of interest policy among the United States, France, and Australia
Bibliographic record
Abstract
"Sunshine" policy, aimed at making financial ties between health professionals and industry publicly transparent, has recently gone global. Given that transparency is not the sole means of managing conflict of interest, and is unlikely to be effective on its own, it is important to understand why disclosure has emerged as a predominant public policy solution, and what the effects of this focus on transparency might be. We used Carol Bacchi's problem-questioning approach to policy analysis to compare the Sunshine policies in three different jurisdictions, the United States, France and Australia. We found that transparency had emerged as a solution to several different problems including misuse of tax dollars, patient safety and public trust. Despite these differences in the origins of disclosure policies, all were underpinned by the questionable assumption that informed consumers could address conflicts of interest. We conclude that, while transparency reports have provided an unprecedented opportunity to understand the reach of industry within healthcare, policymakers should build upon these insights and begin to develop policy solutions that address systemic commercial influence.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".