Rethinking Knowledge Integration in Health Through Digital Experimentation
Notice bibliographique
Résumé
Drawing on different types of knowledge—from clinical research to the experiential knowledge of patients and healthcare professionals—holds significant potential to lead to better quality and relevant healthcare. However, systematically integrating such knowledge has been a long-standing challenge within the framework of Evidence-Based Medicine (EBM) and, more specifically, in the development of clinical and public health guidelines. This highlights the need to better understand and facilitate the integration of diverse forms of knowledge, particularly experiential knowledge, into EBM and guideline development. This thesis explores the potential of digital methods, especially AI-based methods, as a different and potentially innovative approach to supporting this integration. It thereby also examines the insights that experimenting with these methods can offer regarding the inclusion and marginalization of experiential knowledge in healthcare standards. To explore the potential and relevance of these methods in a reflexive and differentiated way, it is argued that they must be situated within the long-standing body of work in sociology and science and technology studies (STS) on knowledge exclusion and inclusion in the field of health. I adopt a transdisciplinary and STS Making & Doing approach, generating practical and theoretical insights into knowledge integration and the role of digital methods through experimenting with these methods in collaboration with various actors during the development of the Dutch public health guidelines on COVID-19 vaccination, scabies, and transgender care. In each case, AI-based methods, particularly from the field of natural language processing, were developed and applied to identify and analyze experiential knowledge shared online by patients, health professionals, and citizens—rendering this knowledge accessible for guideline development. These were complemented by various qualitative methods, including interviews, participant observation, and autoethnography, to better understand the dynamics of knowledge integration and exclusion. The findings demonstrate that AI-based methods are effective in gaining valuable insights into the experiential knowledge of patients, healthcare professionals, and citizens—insights that would otherwise be difficult to obtain. However, their meaningful application requires careful consideration of the dynamics of knowledge inclusion and exclusion. The analyses identify some of the multifaceted mechanisms through which experiential knowledge is marginalized, as well as strategies to support its inclusion—demonstrating that technological solutions alone, including AI-based methods, are insufficient to address the challenge of integrating diverse knowledge in health contexts. Based on these findings, the thesis offers three key lessons for integrating experiential knowledge in epistemically charged healthcare settings: (1) digital methods must be accompanied by dialogical engagement and collaborative knowledge work among the various actors involved; (2) knowledge integration should be conceptualized more broadly, particularly embracing approaches that allow differences to remain unresolved rather than forcing reconciliation and consensus; and (3) strategies that subtly deconstruct or bypass rigid knowledge categories can be as effective as those that explicitly foreground difference. Overall, this thesis seeks to advance conceptual and practical insights into how diverse forms of knowledge can be systematically and meaningfully integrated into medical knowledge production and healthcare practice, as well as the opportunities that innovative digital methods may offer to further support this integration.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».