Using network science to understand the knowledge exchange pathways in health systems research
Notice bibliographique
Résumé
Evidence-informed public policy has demonstrated positive outcomes for populations. Within the health sector, the concept of evidence-informed decision-making (EIDM) suggests that knowledge generated from scientific research will be translated into evidence to support better policy. To facilitate this process, the concept of knowledge translation (KT) was developed within the Canadian health context fewer than 20 years ago. Achieving the aspirational goals of EIDM and KT has proven difficult. Literature reviews have found that only 20% of knowledge transfer and exchange studies discussed real-world application and 14% of health research findings enter day-to-day practice, taking 17-20 years to do so. New knowledge emerges through collaboration. Many aspects of KT involve complex social processes fundamentally embedded in relationships. There is compelling research showing a group’s success in solving complex problems is primarily correlated with the quality of relationships individuals form. Existing frameworks are almost devoid of interpersonal knowledge exchange (KE) networks and therefore only tell part of the story. Epidemiology has embraced network science for quantitatively describing the transmission dynamics of communicable diseases as a contagion phenomenon. This dissertation uses a similar approach to suggest that KE shares fundamental properties with other contagions. The characteristics of individuals as well as the underlying network structure and heterogeneous patterns of combining and exchanging knowledge translates seamlessly. Two applications are used to support this novel contribution. At the macro- level, a bibliometric analysis is used to understand the international co-authorship trends in health policy and systems research (HPSR). The resulting data were used in a network analysis to understand the degree to which economic regions served by HPSR actually participate. At the micro-level, a survey was conducted in a public health agency with an embedded research mandate. The survey captured demographics, knowledge about research and interpersonal networks on which research knowledge flows. These results were used to show the knowledge exchange pathways within the respective networks. Bibliometric and survey outcomes parameterize scalable, generalizable networks. Both macro- and micro- applications use networks to develop strategies and highlight metrics that facilitate meaningful inclusion of the intended end users throughout the research process to improve KE for EIDM.
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 | 0,003 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| 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 ».