NCI-Delirium Model: Co-caring to decrease the incidence of delirium among hospitalized older adults.
Notice bibliographique
Résumé
Delirium negatively impacts the health and wellbeing of older adults and their family carers and contributes to unnecessary high healthcare costs, estimated at $8.8 billion per year in Australia. Improvements to clinical practice are urgently needed, particularly now, as the COVID-19 pandemic is placing an enormous burden on our hospitals. Innovative strategies that support partnerships with family carers have been reported as key to improved patient outcomes and satisfaction with care, particularly for older adults unable to participate in their own care. This is because carers have intimate knowledge of a family member’s cognitive state to identify early subtle changes others might not. Carers also provide valuable reassurance and familiarity to the older adults during their treatment and can orient them to place and time. Founded on our clinical observations, informed by a scoping review of the literature (in press) and co-designed with carers, consumers, clinicians and policy analysts, we developed a new model of delirium care: NCI-Delirium. NCI-Delirium is a validated, scalable, low risk model that values the lived experience of older adults and carers. NCI-Delirium supports the integration of carers as partners in the care of the older adult from admission into the acute care setting. To support the integration of carers as partners, they are offered a web-based Delirium Toolkit that contains: an education package to build awareness and skill development (e.g., delirium risk factors, therapeutic and preventive strategies and non-pharmacological interventions to manage the reorientation of the hospitalised older adult); a 7-item psychometrically tested screening tool designed for carers to identify delirium symptoms and people at risk of delirium; access to support resources (e.g., counselling, social prescriptions, peer-support); and a co-designed discharge plan with the health team. Preliminary results suggest that NCI-Delirium improves health service delirium outcomes. Carer involvement in this research, and the introduction of their perspectives, has elevated carers’ key relationships with clinicians. Our co-designed solution to support the integration of carers as partners in delirium management has improved carers’ caregiving burden, psychological wellbeing and knowledge of delirium. This translatable co-designed model of care provides the much needed rich and robust clinical, implementation and economic evidence, to address the risk factors associated with the prevalence of delirium in hospitalised older adults. It is anticipated that the model will transform how we integrate carers as partners in care to improve clinical practice, patient and carer experiences; and reduce caregiving burden. Globally, the identification and management of risk for delirium is imperative. Delirium is a potentially life-threatening condition, and not well detected in the acute or community care setting. If carers are integrated and supported as partners in care, the capacity to identify symptoms and support the diagnosis of delirium should increase, potentially reducing functional decline, transition to residential aged care facilities and mortality rates. The next steps are to evaluate this co-designed web-based delirium model of care for use by carers in the community and residential aged care setting.
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 | 0,010 | 0,020 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,003 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,003 | 0,006 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 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 ».