Care aides’ job satisfaction affected by COVID-19 pandemic in long-term care settings in northern British Columbia
Bibliographic record
Abstract
,The COVID-19 pandemic impacted healthcare settings, particularly long-term care facilities (LTCFs) that serve vulnerable older adults. Measures implemented to enhance LTCF resident’s safety also had profound effects on staff, especially care aides who provide direct care, yet little is known about their specific experiences during the pandemic. This study aims to shed light on the experiences of care aides working in LTCFs in northern British Columbia during the COVID-19 pandemic, focusing on their job satisfaction. A systematic review using Arksey and O'Malley's (2005) scoping study framework examined four databases: PubMed MEDLINE, CINAHL, Social Work Abstracts, and APA PsycINFO. Secondary qualitative data from eight care aides, collected through one-hour semi-structured interviews, was analyzed using Braun and Clarke's (2006) thematic analysis method. The findings reveal that the COVID-19 pandemic increased workloads and stress levels among care aides, negatively impacting their job satisfaction due to inadequate support systems, disrupted communication, and new safety policies. The study underscores the need for increased staffing, better psychological and financial support, and enhanced communication channels, recommending crisis management training and ongoing education. Further research is needed to evaluate the long-term effects of the COVID-19 pandemic on care aides' job satisfaction and mental health, particularly in rural areas.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| 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".