Implementing a Personalized Integrated Stepped-Care Method (STIP-Method) to Prevent and Treat Neuropsychiatric Symptoms in Persons With Dementia in Nursing Homes: Protocol for a Mixed Methods Study
Notice bibliographique
Résumé
BACKGROUND: Neuropsychiatric symptoms occur frequently in many nursing home residents with dementia. Despite the availability of multidisciplinary guidelines, neuropsychiatric symptoms are often inadequately managed. Three proven effective methods for managing neuropsychiatric symptoms were integrated into a single intervention method: the STIP-Method, a personalized integrated stepped-care method to prevent and treat neuropsychiatric symptoms. The STIP-Method comprises 5 phases of clinical reasoning to neuropsychiatric symptoms and 4 stepped-care interventions and is supported with a web application. OBJECTIVE: This study aims to identify the facilitators and barriers in the implementation of the STIP-Method in nursing homes. METHODS: A mixed methods design within a participatory action research was used to implement the STIP-Method in 4 facilities of 2 Dutch nursing home organizations. In total, we aimed at participation of 160-200 persons with dementia and expected an intervention fidelity of 50% or more, based on earlier studies regarding implementation of effective psychosocial interventions to manage neuropsychiatric symptoms. All involved managers and professionals were trained in the principles of the STIP-Method and in using the web application. An advisory board of professionals, managers, and informal caregivers in each facility supported the implementation during 21 months, including an intermission of 6 months due to the COVID-19 pandemic. In these 6-weekly advisory board meetings, 2 researchers stimulated the members to reflect on progress of the implementation by making use of available data from patient records and the web application. Additionally, the 2 researchers invited the members to suggest how to improve the implementation. Data analysis will involve (1) analysis of facilitators and barriers to the implementation derived from verbatim text reports of advisory board meetings to better understand the implementation process; (2) analysis of patient records in accordance with multidisciplinary guidelines to neuropsychiatric symptoms: personalized, interdisciplinary, and proactive management of neuropsychiatric symptoms; (3) evaluation of the web application in terms of usability scores; (4) pre- and postimplementation analysis of patient records and the web application to evaluate the impact of the STIP-Method, such as changes in neuropsychiatric symptoms and informal caregiver burden. RESULTS: We enrolled 328 persons with dementia. Data collection started in July 2019 and ended in December 2021. The first version of this manuscript was submitted in October 2021. The first results of data analysis are expected to be published in December 2022 and final results in June 2023. CONCLUSIONS: Our study may increase understanding of facilitators and barriers to the prevention and treatment of neuropsychiatric symptoms in nursing home residents with dementia by implementing the integrated STIP-Method. The need for well-designed implementation studies is of importance to provide nursing homes with optimal tools to prevent and treat neuropsychiatric symptoms. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/34550.
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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 | 0,052 | 0,036 |
| Méta-épidémiologie (sens strict) | 0,005 | 0,003 |
| Méta-épidémiologie (sens large) | 0,006 | 0,006 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,004 | 0,003 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,005 | 0,003 |
| Intégrité de la recherche | 0,006 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,041 | 0,007 |
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 ».