Strategies for Self-Care and Support During the COVID-19 Pandemic: Findings From an International Survey of Social Workers in Palliative Care
Bibliographic record
Abstract
The COVID-19 pandemic caused many hospice and palliative care services to reconfigure existing services and invent new ones using technology and remote working. Workloads increased in response to demand and healthcare professionals risked professional burnout, stress and emotional exhaustion. The aim of this study was to conduct secondary analysis of international survey data from palliative care social workers regarding the support received during the COVID-19 pandemic. A cross-sectional online survey-based design was used. Social workers in palliative care were invited to participate via members of international palliative care Social Work networks. The findings are based on data from 278 respondents from 21 countries. Most reported online team meetings, supervision and peer-led group supervision as the main support strategies during the pandemic, yet many indicated they had no time during the pandemic to access support. With hindsight, 43.5% of respondents said they would have done things differently during the pandemic. Most regretted implementing social distancing measures, due to the social isolation, moral distress and impact on grieving relatives. Managers need to help social workers prioritize self-care and proactively engage in support strategies, including supervision, peer-led group supervision and have a better work-life balance that allows time to switch off from work.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".