Job Satisfaction and Well-Being of Care Aides in Long-Term Care During the COVID-19 Pandemic: A Comprehensive Literature Review
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.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".