Providing palliative and end-of-life care in long-term care during the COVID-19 pandemic: a qualitative study of clinicians’ lived experiences
Bibliographic record
Abstract
BACKGROUND: A disproportionate number of COVID-19-related deaths in Canada occurred in long-term care homes, affecting residents, families and staff alike. This study explored the experiences of long-term care clinicians with respect to providing palliative and end-of-life care during the COVID-19 pandemic. METHODS: We used a qualitative research approach. Long-term care physicians and nurse practitioners (NPs) in Ontario, Canada, participated in semistructured interviews between August and September of 2021. Interviews were undertaken virtually, and results were analyzed using thematic analysis. RESULTS: Twelve clinicians (7 physicians and 5 NPs) were interviewed. We identified 5 themes, each with several subthemes: providing a palliative approach to care, increased work demands and changing roles, communication and collaboration, impact of isolation and visitation restrictions, and impact on the providers' personal lives. Clinicians described facing several concurrent challenges, including the uncertainty of COVID-19 illness, staffing and supply shortages, witnessing many deaths, and distress caused by isolation. These resulted in burnout and feelings of moral distress. Previous training and integration of the palliative care approach in the long-term care home, access to resources, increased communication and interprofessional collaboration, and strong leadership mitigated the impact and led to improved palliative care and a sense of pride while facing these challenges. INTERPRETATION: The pandemic had a considerable impact on clinicians caring for residents in long-term care homes at the end of life. It is important to address these lived experiences and use the lessons learned to identify strategies to improve palliative care in long-term care homes and reduce the impact of future pandemics with respect to palliative care.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".