Factors Associated With the Quality of Life of Nursing Home Residents During the COVID-19 Pandemic: A Cross-Sectional Study
Bibliographic record
Abstract
OBJECTIVES: Quality of life (QoL) of nursing home (NH) residents is critical, yet understudied, particularly during the COVID-19 pandemic. Our objective was to examine whether COVID-19 outbreaks, lack of access to geriatric professionals, and care aide burnout were associated with NH residents' QoL. DESIGN: Cross-sectional study (July to December 2021). SETTING AND PARTICIPANTS: We purposefully selected 9 NHs in Alberta, Canada, based on their COVID-19 exposure (no or minor/short outbreaks vs repeated or extensive outbreaks). We included data for 689 residents from 18 care units. METHODS: We used the DEMQOL-CH to assess resident QoL through video-based care aide interviews. Independent variables included a COVID-19 outbreak in the NH in the past 2 weeks (health authority records), care unit-levels of care aide burnout (9-item short-form Maslach Burnout Inventory), and resident access to geriatric professionals (validated facility survey). We ran mixed-effects regression models, adjusted for facility and care unit (validated surveys), and resident covariates (Resident Assessment Instrument-Minimum Data Set 2.0). RESULTS: Recent COVID-19 outbreaks (β = 0.189; 95% CI: 0.058-0.320), higher proportions of emotionally exhausted care aides on a care unit (β = 0.681; 95% CI: 0.246-1.115), and lack of access to geriatric professionals (β = 0.216; 95% CI: 0.003-0.428) were significantly associated with poorer resident QoL. CONCLUSIONS AND IMPLICATIONS: Policies aimed at reducing infection outbreaks, better supporting staff, and increasing access to specialist providers may help to mitigate how COVID-19 has negatively affected NH resident QoL.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".