Technologies to Increase Freedom for People Living With Dementia
Notice bibliographique
Résumé
What Is the Issue? Dementia refers to symptoms affecting cognition (including memory), behaviour, and mood, which can significantly impact daily activities and independence. Conditions that may contribute to the development of dementia include Alzheimer disease (AD), Parkinson disease, and stroke. As the population ages, the number of people in Canada living with dementia will continue to increase. More than 60% of people with dementia live at home rather than in a long-term care facility. Approximately 40% of people aged 80 and older who have dementia reside in long-term care facilities, while others stay in their homes. When living at home in the community, support is needed to maintain safety, independence, and quality of life for both the person living with dementia and their caregiver(s). Most of the assistive technologies used to help support people with dementia to live in their homes are paid for by the user and not by provincial or territorial insurance programs, highlighting issues of equity of access related to income. What Is the Technology? A variety of technologies, including medical devices and consumer electronics, are available that can be used with the intention of helping people living with dementia stay in their homes. Technologies to support the care of people living with dementia can be broadly grouped into 2 categories: technologies related to diagnosis, assessment, and early risk identification and technologies related to management and rehabilitation. This report primarily focuses on technologies that aim to provide management and rehabilitation; these aim to support people with dementia (and their caregivers) – allowing people to live in their homes and communities for longer. These are often classified as assistive devices and include GPS trackers, fall monitoring systems, and connected technologies that can increase and simplify access to services such as food or grocery delivery, pharmacies, and telehealth. What Is the Potential Impact? User-friendly, connected, and effective technologies that allow freedom for people living with dementia may also reduce caregiving stress; however, most randomized trials of assistive technologies have not demonstrated the usefulness of these technologies in real-world settings in a way that support them being formally incorporated into dementia management. The use of supportive technologies may help people living with dementia stay safely in their homes for longer, thereby reducing the burden on long-term care facilities and providing potential savings to the health care system. What Else Do We Need to Know? There is a constant conflict between safety and privacy for people living with dementia who may not always be aware of their current state of cognition and may not be able to provide adequately informed consent for the continued use of monitoring technologies. The evidence suggests further research into the effectiveness of these technologies in real-world settings is required to understand better their usefulness and place in therapy for people living at home with dementia. There remains a lack of consensus on the effectiveness of these technologies and a lack of guidance for their use.
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 | 0,002 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,006 | 0,010 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,004 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,031 | 0,014 |
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 ».