Virtual home-based palliative care during COVID-19: A qualitative exploration of the patient, caregiver, and healthcare provider experience
Bibliographic record
Abstract
BACKGROUND: Due to the COVID-19 pandemic, many community palliative healthcare providers shifted from providing care in a patient's home to providing almost exclusively virtual palliative care, or a combination of in-person and virtual care. Research on virtual palliative care is thus needed to provide evidence-based recommendations aiming to enhance the delivery of palliative care during and beyond the pandemic. AIM: To explore the experiences and perceptions of community palliative care providers, patients and caregivers who delivered or received virtual palliative care as a component of home-based palliative care during the COVID-19 pandemic. DESIGN: Qualitative study using phone and video-based semi-structured interviews. Data were analyzed using thematic analysis. SETTING/PARTICIPANTS: = 18) recruited from sites in Ottawa and Toronto, Ontario, Canada. RESULTS: Overall, participants preferred in-person palliative care compared to virtual care, but suggested virtual care could be a useful supplement to in-person care. The findings are presented in three main themes: (1) Impact of COVID-19 pandemic on community palliative care services; (2) Factors influencing transition from exclusively virtual model of care back to a blended model of care; and (3) Recommended uses and implementation of virtual palliative care. CONCLUSIONS: Incorporating virtual palliative care into healthcare provider practice models (blended care models) may be the ideal model of care and standard practice moving forward beyond the COVID-19 pandemic, which has important implications toward organization and delivery of community palliative care services and funding of healthcare providers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".