The Impact of the COVID-19 Pandemic on Care Aides’ Job Satisfaction in Long-Term Care Facilities in Northern British Columbia: A Qualitative Study
Bibliographic record
Abstract
Background: The COVID-19 pandemic placed unprecedented strain on long-term care facilities (LTCFs), disproportionately affecting care aides who provide essential frontline support. This study explored the impact of the pandemic on care aides’ job satisfaction in LTCFs across Northern British Columbia, a geographically and structurally underserved region. Methods: Eight care aides participated in semi-structured virtual interviews, and data were analyzed using Braun and Clarke’s thematic analysis. Results: Five key themes emerged: (1) work environment and staffing conditions, (2) emotional and psychological burden, (3) communication and team dynamics, (4) resident care and safety, and (5) effects of evolving COVID-19-related policies. Participants consistently described emotional exhaustion, policy fatigue, grief from resident deaths, communication breakdowns, and uncertainty stemming from frequent procedural changes. These stressors were compounded by staffing shortages and limited access to mental health support, especially in rural settings. Conclusion: The findings highlight the urgent need for structural and psychological supports, including responsive leadership, accessible mental health resources, and stable employment policies, to protect care aides’ well-being and ensure quality of care in future public health emergencies.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".