Staff Resiliency in Long-Term Care during the COVID-19 Pandemic: A Qualitative Study
Bibliographic record
Abstract
The COVID-19 pandemic has had a major impact on long-term care facilities (LTCFs). While much attention has been paid to the impact of the pandemic on residents, less attention has been given to the experiences of staff and factors impacting their resilience in facing challenges working in LTCF. This research describes the factors contributing to the resiliency of LTCF staff during the COVID-19 pandemic in northern British Columbia (BC). Transcripts from 53 participants who completed one-hour semi-structured interviews were included and thematic analysis was conducted. All participants had experience working in a LTCF facility in northern BC during the pandemic. The LTCF staff described resilience as the ability to adapt to changing circumstances and protocols, while also maintaining a positive attitude and uplifting spirits during times of adversity. The analysis revealed five key themes influencing staff resilience: (1) availability and provision of resources for staff, (2) leadership and management within LTCFs, (3) social support available to staff, (4) impact of residents’ morale on staff resilience, and (5) personal attributes and characteristics of the staff. Understanding and addressing the five themes can guide the development of targeted strategies and interventions aimed at enhancing staff resilience and well-being during challenging circumstances. By recognizing and addressing the specific needs of LTCF staff, it is possible to improve the overall quality of care provided in LTCF and promote the well-being of both residents and staff. The findings shed light on the interplay of these themes and their profound influence on LTCF staff. Identifying staff’s needs and factors that contribute to their resilience may lower staff turnover, leading to a stronger and more resilient healthcare system, capable of safeguarding vulnerable populations, particularly during times of crisis such as the COVID-19 pandemic.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".