The Influence of Industry Sponsorship on the Research Agenda: A Scoping Review
Notice bibliographique
Résumé
BACKGROUND: Corporate interests have the potential to influence public debate and policymaking by influencing the research agenda, namely the initial step in conducting research, in which the purpose of the study is defined and the questions are framed. OBJECTIVES: We conducted a scoping review to identify and synthesize studies that explored the influence of industry sponsorship on research agendas across different fields. SEARCH METHODS: We searched MEDLINE, Scopus, and Embase (from inception to September 2017) for all original research and systematic reviews addressing corporate influence on the research agenda. We hand searched the reference lists of included studies and contacted experts in the field to identify additional studies. SELECTION CRITERIA: We included empirical articles and systematic reviews that explored industry sponsorship of research and its influence on research agendas in any field. There were no restrictions on study design, language, or outcomes measured. We excluded editorials, letters, and commentaries as well as articles that exclusively focused on the influence of industry sponsorship on other phases of research such as methods, results, and conclusions or if industry sponsorship was not reported separately from other funding sources. DATA COLLECTION AND ANALYSIS: At least 2 authors independently screened and then extracted any quantitative or qualitative data from each study. We grouped studies thematically for descriptive analysis by design and outcome reported. We developed the themes inductively until all studies were accounted for. Two investigators independently rated the level of evidence of the included studies using the Oxford Centre for Evidence-Based Medicine ratings. MAIN RESULTS: We included 36 articles. Nineteen cross-sectional studies quantitatively analyzed patterns in research topics by sponsorship and showed that industry tends to prioritize lines of inquiry that focus on products, processes, or activities that can be commercialized. Seven studies analyzed internal industry documents and provided insight on the strategies the industry used to reshape entire fields of research through the prioritization of topics that supported its policy and legal positions. Ten studies used surveys and interviews to explore the researchers' experiences and perceptions of the influence of industry funding on research agendas, showing that they were generally aware of the risk that sponsorship could influence the choice of research priorities. CONCLUSIONS: Corporate interests can drive research agendas away from questions that are the most relevant for public health. Strategies to counteract corporate influence on the research agenda are needed, including heightened disclosure of funding sources and conflicts of interest in published articles to allow an assessment of commercial biases. We also recommend policy actions beyond disclosure such as increasing funding for independent research and strict guidelines to regulate the interaction of research institutes with commercial entities. Public Health Implications. The influence on the research agenda has given the industry the potential to affect policymaking by influencing the type of evidence that is available and the kinds of public health solutions considered. The results of our scoping review support the need to develop strategies to counteract corporate influence on the research agenda.
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 | 0,150 | 0,421 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,003 |
| Méta-épidémiologie (sens large) | 0,006 | 0,006 |
| Bibliométrie | 0,046 | 0,039 |
| Études des sciences et des technologies | 0,004 | 0,005 |
| Communication savante | 0,015 | 0,016 |
| Science ouverte | 0,003 | 0,007 |
| Intégrité de la recherche | 0,007 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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 ».