Dementia-Friendly “Design”: Impact on COVID-19 Death Rates in Long-Term Care Facilities Around the World
Bibliographic record
Abstract
Persons with dementia (PWD) make up a large portion of the long-term care (LTC) population the world over. Before a global pandemic swept the world, governments and healthcare providers struggled with how to best care for this unique population. One of the greatest challenges is a PWD's tendency to "walk with purpose" and exhibit unsafe wayfinding and elopement, which places them at risk of falls and injury. Past solutions included increased use of restraints and pharmacological interventions, but these have fallen out of favor over the years and are not optimal. These challenges put enormous strain on staff and caregivers, who are often poorly trained in dementia care, underpaid, overworked, and overstressed. PWD are impacted by these stresses, and unmet needs in LTC places an even greater stress on them and increases their risks of morbidity and mortality. The physical design of their environments contributes to the problem. Old, institutionalized buildings have poor lighting, poor ventilation, long dead-end hallways, poor visual cues, lack of home-like décor, shared bedrooms and bathrooms, and are often dense and overcrowded. These design elements contribute to the four 'A's' of dementia: apathy, anxiety, agitation, and aggression, and they also contributed to the rapid spread of COVID-19 in these facilities the world over. In this review, we present current "dementia friendly" design models in the home, community, and LTC, and argue how they could have saved lives during the pandemic and reduced the stresses on both the dementia resident and the caregiver/staff.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".