ASSOCIATION OF ASSISTED LIVING HOMES PREPAREDNESS AND RESPONSES TO THE COVID-19 PANDEMIC WITH RESIDENT LONELINESS
Bibliographic record
Abstract
Abstract Loneliness has been an ongoing problem among Assisted living (AL) residents and has worsened during the COVID-19 pandemic. Loneliness is associated with an increased risk of mental and physical illness, cognitive decline, suicidal behavior, and all-cause mortality. However, the impact of the pandemic on AL residents’ loneliness is poorly understood. We surveyed 42 AL homes in Alberta to understand whether resident characteristics and homes’ pandemic preparedness and response to the pandemic were associated with resident loneliness. The surveys reflected pandemic waves 1 (Mar-Jul 2020) and 2 (Nov 2020-Feb 2021) and were linked to Resident Assessment Instrument (RAI) records of residents who lived in these homes during these periods. Using generalized estimating equation models, we assessed whether resident characteristics, residents’ social relationships, and home preparedness for and responses to the pandemic were associated with resident loneliness (measured as present or absent based on RAI items). Our sample included 1,828 residents (wave 1: 890, wave 2: 938). Almost 13% of the residents reported loneliness (wave 1: 11.2%, wave 2: 14.2%). Depressive symptoms (odds ratio [OR]=2.73, 95% CI, 1.9-3.9) daily/excruciating pain (OR=1.97, 95% CI, 1.4-2.8), and cognitive impairment (OR=0.38, 95% CI, 0.2-0.6) were significantly associated with loneliness. Caregiver availability, hours of caregiver help, and home preparedness were not associated with loneliness, but more communication with caregivers (OR=2.19, 95% CI, 1.1-4.2) and same or improved staff morale (OR=0.61, 95% CI, 0.4-0.9) were. Improving staff morale and communication with caregivers is crucial in addressing resident loneliness.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".