Mitigating apathy among older adults with and without dementia across long term care and community settings: A multimethod study
Notice bibliographique
Résumé
,Background: Apathy, characterized by reduced interest in activities and social interaction, is a prevalent yet often underrecognized condition among older adults in long-term care facilities (LTCF) and community settings. It is associated with rapid cognitive decline, functional impairments, and decreased life expectancy. Despite its impact, apathy is frequently misdiagnosed or conflated with other conditions, such as depression and dementia, leading to inadequate intervention strategies. While various non-pharmacological interventions have been proposed, little is known about the effectiveness of eBook clubs as a non-pharmacological option for mitigating apathy. This study seeks to address the overarching research question: "What are the barriers, facilitators, prevalence, and risk factors of apathy among older adults in LTCF and community settings, and how effective is an eBook club intervention in mitigating apathy in these populations?" Methods: This multimethod study employed a series of research designs to explore the multidimensional aspects of apathy and its mitigation guided by the Biopsychosocial Model of Health and Illness and Socioemotional Selectivity Theory. The prevalence and predictors of apathy were analyzed cross-sectionally using the InterRAI Minimum Dataset (MDS 2.0) from the Canadian Institute for Health Information, covering LTCF residents admitted between 2015 and 2019. A pre- and post-quasi-experimental multi methods design was used to assess the effectiveness of an eBook club intervention among LTCF residents and community-dwelling older adults in four rural communities in Northern British Columbia. The intervention’s impact was measured by comparing apathy levels before and after participation. Results: Findings highlight key barriers to apathy care, including the lack of a standardized definition, limited awareness, symptom overlap with other disorders, and methodological challenges in clinical trials. Facilitators that promote effective apathy management include caregiver involvement, professional training, and the adoption of innovative screening and intervention methods. Apathy was prevalent in 12.5% of newly admitted LTCF residents (N = 157,596) and 13.1% of those with Alzheimer’s disease and related dementias (N = 97,789). Cognitive impairment was identified as the strongest predictor of apathy among the general LTCF population, while depression was the most significant risk factor among residents with dementia. The eBook club intervention demonstrated positive effects, leading to improved social engagement, cognitive stimulation, and emotional well-being among LTCF residents and community-dwelling older adults. Conclusion: Understanding the barriers, facilitators, prevalence, and risk factors of apathy are essential for developing effective interventions. The findings suggest that structured, accessible, and low-cost programs, such as eBook clubs, have the potential to mitigate apathy in both LTCF and community settings. This study underscores the need for enhanced screening, targeted interventions, and policy-driven efforts to improve apathy care and promote well-being of residents in LTCF and community dwelling older adults.
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 | 0,011 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,003 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,004 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».