Large-scale implementation of stroke early supported discharge: the WISE realist mixed-methods study
Notice bibliographique
Résumé
Background In England, the provision of early supported discharge is recommended as part of an evidence-based stroke care pathway. Objectives To investigate the effectiveness of early supported discharge services when implemented at scale in practice and to understand how the context within which these services operate influences their implementation and effectiveness. Design A mixed-methods study using a realist evaluation approach and two interlinking work packages was undertaken. Three programme theories were tested to investigate the adoption of evidence-based core components, differences in urban and rural settings, and communication processes. Setting and interventions Early supported discharge services across a large geographical area of England, covering the West and East Midlands, the East of England and the North of England. Participants Work package 1: historical prospective patient data from the Sentinel Stroke National Audit Programme collected by early supported discharge and hospital teams. Work package 2: NHS staff ( n = 117) and patients ( n = 30) from six purposely selected early supported discharge services. Data and main outcome Work package 1: a 17-item early supported discharge consensus score measured the adherence to evidence-based core components defined in an international consensus document. The effectiveness of early supported discharge was measured with process and patient outcomes and costs. Work package 2: semistructured interviews and focus groups with NHS staff and patients were undertaken to investigate the contextual determinants of early supported discharge effectiveness. Results A variety of early supported discharge service models had been adopted, as reflected by the variability in the early supported discharge consensus score. A one-unit increase in early supported discharge consensus score was significantly associated with a more responsive early supported discharge service and increased treatment intensity. There was no association with stroke survivor outcome. Patients who received early supported discharge in their stroke care pathway spent, on average, 1 day longer in hospital than those who did not receive early supported discharge. The most rural services had the highest service costs per patient. NHS staff identified core evidence-based components (e.g. eligibility criteria, co-ordinated multidisciplinary team and regular weekly multidisciplinary team meetings) as central to the effectiveness of early supported discharge. Mechanisms thought to streamline discharge and help teams to meet their responsiveness targets included having access to a social worker and the quality of communications and transitions across services. The role of rehabilitation assistants and an interdisciplinary approach were facilitators of delivering an intensive service. The rurality of early supported discharge services, especially when coupled with capacity issues and increased travel times to visit patients, could influence the intensity of rehabilitation provision and teams’ flexibility to adjust to patients’ needs. This required organising multidisciplinary teams and meetings around the local geography. Findings also highlighted the importance of good leadership and communication. Early supported discharge staff highlighted the need for collaborative and trusting relationships with patients and carers and stroke unit staff, as well as across the wider stroke care pathway. Limitations Work package 1: possible influence of unobserved variables and we were unable to determine the effect of early supported discharge on patient outcomes. Work package 2: the pragmatic approach led to ‘theoretical nuggets’ rather than an overarching higher-level theory. Conclusions The realist evaluation methodology allowed us to address the complexity of early supported discharge delivery in real-world settings. The findings highlighted the importance of context and contextual features and mechanisms that need to be either addressed or capitalised on to improve effectiveness. Trial registration Current Controlled Trials ISRCTN15568163. Funding This project was funded by the National Institute for Health Research (NIHR) Health Services and Delivery Research programme and will be published in full in Health Services and Delivery Research ; Vol. 9, No. 22. See the NIHR Journals Library website for further project information.
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 | 0,004 | 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 | 0,000 | 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 ».