Collaborations between health services and educational institutions to develop research capacity in health services and health service staff: a systematic scoping review
Notice bibliographique
Résumé
BACKGROUND: Participation of health service staff in research improves health outcomes and adherence to clinical guidelines. To increase research participation, many health services seek to build research capacity which adds to the development of individual and organisational skills and abilities in order to conduct health research. Numerous approaches to research capacity building have been trialed with inter- and intra-institutional, or university-health service collaborative approaches being frequently described strategies. University-health service research collaborations have potential for high impact and mutual benefit, by harnessing respective strengths across both organisations. However, the range and scope of research capacity building approaches, including their relative value and success have not been consolidated. The aim of this review was to examine and describe the collaborative strategies employed by health services in conjunction with educational partners to enhance the research capability of health service staff. METHODS: The scoping review framework by Arksey and O'Malley was used to inform the review method. A systematic search was conducted of four major databases: Medline, CINAHL, Embase, and Cochrane, focusing on publications after 1995. Inclusion and exclusion criteria were established through iterative team discussions. The two-stage screening process and data extraction was managed in Covidence. Collaboration, Research Capacity, Health Services, and Health workforce were the primary concepts, contexts and populations guiding the search. RESULTS: Of the 1462 studies identified, 61 were selected for the review. These studies reported on partnerships between universities and health services with a specific focus on building research capacity of health service staff. Studies predominantly hailed from Australia, USA, UK, and Canada. Collaboration approaches varied and leveraged different activities to build research capacity included training, mentoring, shared funding, and networking. Training partnerships emerging as the most prevalent. Findings emphasised the importance of localisation in approaches, with some studies indicating the intrinsic value of such collaborations for both partners involved. Despite the emphasis on individual interventions like training and mentoring, team-level interventions were notably scarce. CONCLUSION: This review highlights the diverse range of approaches in research capacity building collaborations between health services and educational partners. It advocates for a shared understanding of goals, highlighting the critical nature of relationship-building and the pivotal role of sustainable infrastructure in long-term collaboration success. Future directions should consider the tangible impacts of these models on clinical outcomes.
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 | 0,078 | 0,225 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,009 | 0,008 |
| Bibliométrie | 0,034 | 0,035 |
| Études des sciences et des technologies | 0,003 | 0,003 |
| Communication savante | 0,009 | 0,010 |
| Science ouverte | 0,004 | 0,006 |
| Intégrité de la recherche | 0,004 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».