THE ASSOCIATION OF COVID-19 OUTBREAKS AND OTHER FACTORS WITH NURSING HOME RESIDENTS’ QUALITY OF LIFE
Bibliographic record
Abstract
Abstract Nursing home (NH) residents’ Quality of life (QoL) is an important goal of care. However, it is understudied, particularly during the COVID-19 pandemic. Our objective was to examine whether COVID-19 outbreaks, care aide emotional exhaustion, and lack of resident access to geriatric professionals were associated with NH residents’ QoL. In this cross-sectional study (Jul-Dec 2021), we purposefully selected 9 NHs in Alberta, Canada based on their COVID-19 exposure (no or minor/short outbreaks vs repeated or extensive outbreaks). Using a validated questionnaire (DEMQOL-CH), we assessed dementia-specific QoL of 689 residents from 18 care units through video-based interviews with care aides. Independent variables included COVID-19 outbreak in the NH in the last 2 weeks (health authority records), proportion of care aides on a care unit with high emotional exhaustion scores (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 characteristics (validated facility and care unit surveys), and resident covariates (Resident Assessment Instrument – Minimum Data Set 2.0). COVID-19 outbreaks within two weeks of the data collection (β=0.189, 95% confidence interval [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. Policies aimed at reducing infection outbreaks, better supporting care staff, and increasing access to geriatric specialists, may help to mitigate negative effects of COVID-19 NH residents’ QoL.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".