“It took so much of the humanness away”: Health care professional experiences providing care to dying patients during COVID-19
Bibliographic record
Abstract
COVID-19 has affected healthcare in profound and unprecedented ways, distorting the experiences of patients and healthcare professionals (HCPs) alike. One area that has received little attention is how COVID-19 affected HCPs caring for dying patients. The goal of this study was to examine the experiences of HCPs working with dying patients during the COVID-19 pandemic. Between July 2020–July 2021, we recruited HCPs (N = 25) across Canada. We conducted semi-structured interviews, using a qualitative study design rooted in constructivist grounded theory methodology. The core themes identified were the impact of the pandemic on care utilization, the impact of infection control measures on provision of care, moral distress in the workplace, impact on psychological wellbeing, and adaptive strategies to help HCPs manage emotions and navigate pandemic imposed changes. This is the first Canadian study to qualitatively examine the experiences of HCPs providing care to dying patients during the COVID-19 pandemic. Implications include informing supportive strategies and shaping policies for HCPs providing palliative care.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.024 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| 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".