GRIEF AND BEREAVEMENT SUPPORT FOR STAFF IN LONG-TERM CARE HOMES: IDENTIFYING BARRIERS AND FACILITATORS
Bibliographic record
Abstract
Abstract Admissions of older adults to long-term care are occurring at more advanced ages with more complex care needs, resulting in shorter lengths of stay before death and an increased demand for a palliative approach to care. However, long-term care staff often lack access to sustainable educational, organizational, and interpersonal support in end-of-life care, and must learn on the job while facing death in the workplace. To address this gap, we conducted a scoping review of literature on grief and bereavement support for long-term care staff. Following PRISMA-Scoping Review guidelines and the six-step framework by Levac and colleagues, social sciences databases were searched and data were analyzed through narrative synthesis and the creation of infographics. Fifty-eight studies met inclusion criteria. Strategies, interventions, and rituals were organized into five domains:1) Cultural Attitudes Toward Death and Unmet Need for Support; 2) Organizational Policy and Practice; 3) Formal Peer-support; 4) Informal Peer-support; and 5) Individual Beliefs and Self-Care Practices influenced by temporal aspects. Identifying barriers and facilitators of grief support for long-term care staff, and hospice and palliative care specialists’ practices, can offer more holistic and effective approaches to long-term care staff wellness to combat burnout and staff turnover. Grief rituals present scalable, holistic approaches fostering long-term care staff wellness that could be explored in future research to collaboratively build long-term care staff capacity for person-centered end-of-life 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 | 0.031 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".