Collaboration between biomedical research and community-based primary health care actors in chronic disease management: a scoping review
Notice bibliographique
Résumé
Abstract Background Collaboration between biomedical research and community-based primary health care actors is essential to translate evidence into clinical practice. However, little is known about the characteristics and impacts of implementing collaborative models. Thus, we sought to identify and describe collaboration models that bridge biomedical research and community-based primary health care in chronic disease management. Methods We conducted a scoping review using Medline, Embase, Web of Science, and Cochrane Library from inception to November 2020, to identify studies describing or evaluating collaboration models. We also searched grey literature, screened reference lists, and contacted experts to retrieve further relevant references. The list of studies was then refined using more specific inclusion and exclusion criteria. Two reviewers independently selected studies and extracted relevant data (characteristics of studies, participants, collaborations, and outcomes). No bias assessment was performed. A panel of experts in the field was consulted to interpret the data. Results were presented with descriptive statistics and narrative synthesis. Results Thirteen studies presenting 20 unique collaboration models were included. These studies were conducted in North America ( n = 7), Europe ( n = 5) and Asia ( n = 1). Collaborations were implemented between 1967 and 2014. They involved a variety of profiles including biomedical researchers ( n = 20); community-based primary health care actors ( n = 20); clinical researchers ( n = 15); medical specialists ( n = 6); and patients, citizens, or users ( n = 5). The main clinical focus was cardiovascular disease ( n = 8). Almost half of the collaborations operated at an international level ( n = 9) and the majority adopted either a network ( n = 7) or hierarchical structure ( n = 6). We identified significant implementation barriers (lack of knowledge, financial support, and robust management structure) and collaboration facilitators (partnership, cooperation, multidisciplinary research teams). Out of the 20 included collaboration models, seven reported measurable impact. Conclusion We identified a large variety of collaboration models representing several clinical and research profiles and fields of expertise. As they are all based in high-income countries, further research should aim to identify collaborations in low-income countries, to determine which models and/or characteristics, could better translate evidence into clinical practice in these contexts.
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 | 0,008 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,005 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».