Death in Long-Term Care: Focus Groups and Interviews Identify Strategies to Alleviate Staff Burnout
Bibliographic record
Abstract
<strong>Context:</strong> Interdisciplinary long-term care staff are being challenged with increasing numbers of resident deaths as well as complex resident and family needs. Studies warn that staff responses to the stress generated by residents’ deaths can lead to increased ill health, sick time, burnout, and attrition. <strong>Objectives:</strong> To alleviate and prevent workplace stress and burnout in staff related to long-term care resident deaths. <strong>Methods:</strong> Participatory action research design. Qualitative individual interviews and focus groups were carried out within five long-term care homes, Vancouver, British Columbia, Canada. <strong>Findings:</strong> Two key themes emerged: Challenges Staff Experienced and Supporting Action Strategies. Challenges are reported under five sub-themes: 1) Differing Expectations, 2) Communication, 3) Acknowledgement, 4) Support, and 5) Education. Supporting Action Strategies to minimize the impact of resident death on staff are presented under four sub-themes: 1) the Individual: Practice self-care, awareness, mindfulness; 2) Team: Enhance end-of-life comfort for residents, strengthen support for families, maximize the use of palliative and spiritual care; 3) Organization: Nurture supportive leadership, improve communication, education, resources and 4) Higher learning: Build palliative care/emotional preparation into the curriculum and promote long-term care as a specialist area of healthcare. <strong>Limitations:</strong> Results may not generalize to other practice contexts; long-term care homes studied are part of a faith-based organization. <strong>Implications:</strong> Long-term care policy and system changes are needed to support interdisciplinary care staff and provide them with tools, resources, and supports to prevent burnout and cope with the increasing stress of working in long-term 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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".