Toward Optimal Management of Behavioral and Psychological Symptoms of Dementia: Insights From a COVID-19 Pandemic Experience
Bibliographic record
Abstract
The first wave of SARS-CoV-2 has deeply affected long term care facilities in the province of Quebec. In response, governmental officials took protective measures, such as suspending visits and activities and even requiring residents to self-isolate to their room. Consequently, residents with major cognitive impairments were cut from their routine as well as from significant social interactions, support, and stimulation essential to their well-being. This isolation negatively affected many residents. For some of them, the loss of bearings resulted in newly or deteriorated behavioral and psychological symptoms of dementia (BPSD). These residents were then more at risk of contracting the virus or contaminating others. To face this challenge, hotels in the Greater Montreal area were transformed into temporary care facilities. As members of a multidisciplinary team specialized in the management of BPSD, we were asked to support the redeployed staff who had little experience in this domain. In this paper, we present the innovative tools implemented in this uncommon work setting. We also discuss factors identified as facilitating the care and treatment of people with BPSD. This experience leads us to propose avenues toward better BPSD management.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".