Comparing quality of care outcomes between assisted living and nursing homes before and during the <scp>COVID</scp> ‐19 pandemic
Bibliographic record
Abstract
BACKGROUND: While assisted living (AL) and nursing home (NHs) residents in share vulnerabilities, AL provides fewer staffing resources and services. Research has largely neglected AL, especially during the COVID-19 pandemic. Our study compared trends of practice-sensitive, risk-adjusted quality indicators between AL and NHs, and changes in these trends after the start of the pandemic. METHODS: This repeated cross-sectional study used population-based resident data in Alberta, Canada. Using Resident Assessment Instrument data (01/2017-12/2021), we created quarterly cohorts, using each resident's latest assessment in each quarter. We applied validated inclusion/exclusion criteria and risk-adjustments to create nine quality indicators and their 95% confidence intervals (CIs): potentially inappropriate antipsychotic use, pain, depressive symptoms, total dependency in late-loss activities of daily living, physical restraint use, pressure ulcers, delirium, weight loss, urinary tract infections. Run charts compared quality indicators between AL and NHs over time and segmented regressions assessed whether these trends changed after the start of the pandemic. RESULTS: Quarterly samples included 2015-2710 AL residents and 12,881-13,807 NH residents. Antipsychotic use (21%-26%), pain (20%-24%), and depressive symptoms (17%-25%) were most common in AL. In NHs, they were physical dependency (33%-36%), depressive symptoms (26%-32%), and antipsychotic use (17%-22%). Antipsychotic use and pain were consistently higher in AL. Depressive symptoms, physical dependency, physical restraint use, delirium, weight loss were consistently lower in AL. The most notable segmented regression findings were an increase in antipsychotic use during the pandemic in both settings (AL: change in slope = 0.6% [95% CI: 0.1%-1.0%], p = 0.0140; NHs: change in slope = 0.4% [95% CI: 0.3%-0.5%], p < 0.0001), and an increase in physical dependency in AL only (change in slope = 0.5% [95% CI: 0.1%-0.8%], p = 0.0222). CONCLUSIONS: QIs differed significantly between AL and NHs before and during the pandemic. Any changes implemented to address deficiencies in either setting need to account for these differences and require monitoring to assess their impact.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".