Exploring neurologists’ perspectives: barriers and facilitators in implementing cognitive care planning
Notice bibliographique
Résumé
The global population is aging at an unprecedented rate, with a significant increase in the prevalence of age-related cognitive disorders. It has been estimated that the number of those aged ≥60 years will increase from 1 billion in 2020 to 2.1 billion in 2050 (World Health Organization, 2024). Cognitive decline and neurodegenerative diseases such as dementia are among the most critical health issues affecting these older adults, posing substantial threats to their independence and quality of life (Steinmetz et al., 2024). For example, the number of people with Alzheimer’s disease and other dementias (ADRD) will nearly triple to more than 152 million by 2050 (Nichols et al., 2022). Neurodegenerative diseases such as ADRD not only impact individuals but also place a heavy burden on their families, caregivers, and healthcare systems worldwide (Chen et al., 2024). Promoting cognitive health in aging populations is therefore a priority in global health initiatives. Cognitive care planning (CCP) is a proactive, structured approach to managing cognitive health, particularly in individuals at risk of or already experiencing cognitive decline (Alzheimer’s Association, n.d.b; Livingston et al., 2024). CCP is covered by Medicare (Centers for Medicare & Medicaid Services, 2025) and was initially introduced through the Health Outcomes, Planning, and Education (HOPE) for Alzheimer’s Act (Alzheimer’s Association, n.d.c). Specifically, CCP involves a dedicated appointment of an hour with a healthcare provider to comprehensively evaluate cognitive abilities, confirm or establish a diagnosis such as dementia or Alzheimer’s disease, and create a tailored care plan (Centers for Medicare & Medicaid Services, 2025). The advantages of CCP include promoting comprehensive assessment, early detection, personalized care strategies, and regular monitoring to optimize cognitive function and delay the progression of cognitive impairments (Alzheimer’s Association, n.d.b; Livingston et al., 2024). Nevertheless, despite its potential benefits, the utilization of CCP in clinical settings is inconsistent and often limited (Piers et al., 2018). Previous research on the implementation of CCP has predominantly studied CCP as a component of advance care planning among patients with neurodegenerative diseases (Canevelli et al., 2024; Cheong et al., 2015; Dassel et al., 2023), which is done to ensure individuals’ autonomy by enabling them to express their wishes for the future (Cheong et al., 2015). The motivations, facilitators, and barriers related to implementing advance care planning, however, are fundamentally different from CCP, with the primary goal of early detection and planning (Alzheimer’s Association, n.d.b). Despite being covered by Medicare, the implementation of CCP for early detection and prevention of ADRD has not been commonly performed. To our knowledge, there is a lack of research that examines why this is the case. Meanwhile, prior literature points to the need for more comprehensive research on the practical aspects of CCP, including resource allocation, interdisciplinary collaboration, and patient engagement (Reuben et al., 2025).
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 | 0,048 | 0,103 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,017 | 0,007 |
| Communication savante | 0,014 | 0,011 |
| Science ouverte | 0,004 | 0,013 |
| Intégrité de la recherche | 0,007 | 0,018 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,002 |
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 ».