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
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,062 | 0,063 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,008 | 0,007 |
| Communication savante | 0,005 | 0,008 |
| Science ouverte | 0,001 | 0,008 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».