What features should an effective system for declaring and managing conflicts of interest in healthcare have? An adapted Delphi study of key stakeholders in the UK
Bibliographic record
Abstract
OBJECTIVE: To identify views and establish agreements of key stakeholders on the features of an effective system for declaring and managing conflicts of interest in healthcare. DESIGN: A modified Delphi study consisting of two surveys and semi-structured interviews. Surveys included closed and free-text questions. SETTING AND PARTICIPANTS: UK, purposefully and generally invited participants including academics, researchers, healthcare professionals, regulators, patients and citizens from 10 countries, during 25 August 2024 and 20 January 2025. MAIN OUTCOME MEASURES: Quantitative and qualitative analysis of two surveys and 21 interviews. Descriptive statistics were used to describe the sample and analyse closed survey questions. Thematic analysis was used to analyse free-text survey responses and interview data. Results were synthesised to describe the perceived importance and purposes of declaration of interest systems. RESULTS: In the first survey round, 616 invitations were sent, along with social media advertisements. 237 questionnaires were returned and 200 full responses were analysable. 129 respondents consented to recontact on the online form. In the interview round, 37 invitations were sent and 21 interviews completed (response rate 59.5%). Invitations for the second survey were sent to all 129 participants who consented to recontact. 91 responses were received and 89 questionnaires were analysable (response rate 82%). Features of ideal systems to declare and manage the interests of healthcare professionals identified by participants were categorised under seven themes: regulatory issues, the healthcare environment, human vices, professional virtues, the use of judgement, features of a better system and patients and public. There was broad agreement on the need for transparency and clarity in declaration systems. The most agreed features were: clarity on what information was needed; it should be a centralised 'deposit' for all declarations; it should be publicly accessible, educating and informing people accessing and using the register. Having a lifelong personal identifier, some flexibility in declarations and some privacy features were also rated highly. Respondents were less concerned about scrutiny or a loss of trust. Small numbers of participants raised concerns about serious adverse effects, including loss of privacy, personal safety and the potential of information to contribute to conspiracy theories. There were also major disagreements between participants concerning whether or not healthcare professionals should work with industry, and whether conflicts of interest from working with industry can be safely managed. Individuals with each perspective felt they were acting ethically. CONCLUSIONS: While many agreements were identified, disagreements were also found. If improved declaration systems are to be accepted by professionals and useful to regulators, patients and citizens, the potential for benefit and harm from new declaration systems must be addressed. REGISTRATION DETAILS: Prepublished, Open Science Framework https://osf.io/fbj5n.
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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 | 0.062 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.008 |
| 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".