Exploring neurologists’ perspectives: barriers and facilitators in implementing cognitive care planning
Bibliographic record
Abstract
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).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.048 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.007 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.007 | 0.018 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".