Effectively Caring for Individuals With Behavioral and Psychological Symptoms of Dementia During the COVID-19 Pandemic
Bibliographic record
Abstract
The COVID-19 pandemic has significantly affected the elderly and particularly individuals with Alzheimer's disease and related disorders (ADRD). Behavioral and psychological symptoms of dementia (BPSD) are heterogeneous and common in individuals with ADRD and are associated with more severe illness. However, unlike the cognitive symptoms of ADRD that are usually progressive, BPSD may be treatable. Individuals with BPSD are facing unique challenges during the pandemic due to the inherent nature of the illness and the biological and psychosocial impacts of COVID-19. These challenges include a higher risk of severe COVID-19 infection in individuals with BPSD due to their frailty and medical vulnerability, difficulty participating in screening or testing, and adhering to infection control measures such as physical distancing. Further, biological effects of COVID-19 on the brain and its psychosocial impact such as isolation and disruption in mental health care are likely to worsen BPSD. In this paper, we discuss these challenges and strategies to manage the impact of COVID-19 and to effectively care for individuals with BPSD in community, long-term care, or hospital settings during the pandemic. Despite the ongoing uncertainty associated with this pandemic, we can reduce its impact on individuals with BPSD with a proactive approach.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".