Job Satisfaction and Well-Being of Care Aides in Long-Term Care During the COVID-19 Pandemic: A Comprehensive Literature Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The COVID-19 pandemic greatly impacted care aides in long-term care facilities (LTCFs), exacerbating existing challenges and introducing new stressors that profoundly affected their job satisfaction, mental health, and overall well-being. This study investigates these multifaceted effects by conducting a comprehensive literature review of 18 studies from 2020 to 2023 across multiple countries. The findings reveal that care aides, mostly older and female and often immigrants with limited formal education, faced increased workloads, emotional exhaustion, physical fatigue, anxiety, and heightened stress levels during the pandemic. These factors led to decreased job satisfaction, higher burnout rates, and further pressure on LTCFs. The review emphasizes the need for strong support systems and targeted interventions, including mental health resources, counseling, adequate personal protective equipment (PPE), effective workload management, professional development opportunities, fair compensation, and supportive work environments. Addressing these issues is crucial for maintaining a stable and effective LTC workforce, improving care outcomes for residents, and enhancing the healthcare system’s resilience against future challenges.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it