Quality of Care during the COVID-19 Pandemic: A Qualitative Exploration of Bereaved Caregivers’ Experiences at a Hospice Residence
Bibliographic record
Abstract
Background: An essential component of residential hospice care is the provision of high quality palliative care. The perspectives of quality of care from palliative care patients and bereaved caregivers have increasingly been studied to guide quality improvements. Aim: The study aimed to explore the experiences and perceptions of bereaved caregivers of patients who died in residential hospice during the coronavirus disease-19 (COVID-19) pandemic to determine perceptions of quality of care, caregiver grief and bereavement, and the impact of the COVID-19 pandemic. Design: Qualitative exploratory study using secure, web-based semistructured interviews. Data were analyzed using thematic analysis. Setting/Participants: A total of 15 bereaved caregiver participants were recruited from two residential hospice sites in Ottawa, Ontario. Results: Participants identified several factors that impacted the quality of care in residential hospice, including the impact of the COVID-19 pandemic itself. The findings are presented in three main themes: (1) quality of residential hospice end-of-life care; (2) caregiver perceptions of their grief and bereavement; and (3) impact of the COVID-19 pandemic on hospice quality of care and caregiver bereavement. Conclusions: The COVID-19 pandemic had a significant impact on the patient and caregivers experience of hospice, including perception of quality of care and caregiver experience of grief and bereavement.
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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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 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".