Stronger together:Empowering rural research through education-health partnerships
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Notice bibliographique
Résumé
Background<br/><br/>Rural, remote and regional (R3) healthcare is shaped by unique settings, population characteristics and health conditions. Preparing the future R3 workforce for these contextual challenges requires an understanding of these unique attributes, something that is best achieved by R3-specific research. Research active health services are associated with better patient outcomes, but with limited numbers of experienced researchers in R3 areas, health services may seek to build research capacity. Research capacity building (RCB) is a process that adds to individual and organisational skills and abilities to conduct health research, shaping the future of rural and remote healthcare.<br/><br/>Multiple approaches to RCB have been implemented and collaborations between educational institutions and health services is one example. These collaborations can harness respective strengths across organisations, with potential for high impact and mutual benefit.<br/><br/>To understand these collaborations, this scoping review examines and describes the collaborative strategies employed to enhance the research capacity of health service staff. This abstract focuses on the R3 partnerships identified in the review whilst drawing on learnings from the wider review.<br/><br/>Methods<br/><br/>Informed by Arksey and O’Malley’s scoping review framework, we systematically searched four major databases: Medline, CINAHL, Embase, and Cochrane, focusing on publications after 1995. Collaboration, Research Capacity, Health Services, and Health Workforce were the primary concepts, contexts and populations guiding the search. These concepts were expanded using synonyms that were decided through team discussion. We established inclusion and exclusion criteria through iterative team discussions and used Covidence throughout the two-stage screening process and data extraction.<br/><br/>Results<br/><br/>From 1462 initially identified studies, 61 were selected for the review. Nineteen studies specifically focused on rural partnerships between educational institutions and health services for building research capacity of health service staff. Studies predominantly hailed from Australia, USA, UK, and Canada with rural models largely from Australia and Canada. Collaborative approaches to RCB included training, mentoring, shared funding, and networking. Rural partnerships focused on training health staff as first-time researchers, reflecting the emerging research skill set in R3 contexts and the need to build research from a widely distributed base. In rural areas funding partnerships were less prominent, reflecting both the low quanta of rural research funding and a tendency for rural research collaborations to rely on goodwill and existing relationships. Our findings emphasise the importance of tailoring approaches to local contexts, something that adds intrinsic value for both collaboration partners. Despite the known value of team-based healthcare, approaches focused largely on individual interventions like training and mentoring, with team-level interventions notably scarce.<br/><br/>Conclusion<br/><br/>Our review highlights that a diverse range of approaches have been implemented to develop research capacity through collaborations between health services and educational partners. For R3 partnerships, relationships are at the heart of collaborations. We recommend focussing on building from existing relationships and ensuring mutual goals are established early. Despite a resource-constrained environment, long-term collaborative success does rely on sustainable infrastructure and this must be a focus for partners to work towards. Working together can enable partnerships that prepare the R3 workforce for future research activity.
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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 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,008 | 0,001 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 découle