Intensifying Relational Care: The Challenge of Dying in Long-Term Residential Care
Bibliographic record
Abstract
Although the culture change movement has sought to transform residential care facilities from warehouses of death into homes for living, there is growing recognition of the need to address dying within these settings. Drawing on data from an international and interdisciplinary study, this paper explores the state of end-of-life care in residential care facilities, identifying barriers to the provision of compassionate care for the dying, as well as promising practices and areas for future inquiry. Interviews with staff and researcher observations at 20 nursing homes in Canada, Germany, Norway, Sweden, the United States, and the United Kingdom were analyzed. Six themes were identified: the growing need for end-of-life care; the challenge of identifying a dying phase; the importance of open communication about death; the need to address bereavement of both families and staff; the need for additional training and resources; and the inadequacy of current models of care. Taken together, these findings suggest that dying intensifies the need for relational care, a type of care residential care facilities have been struggling to provide. However, while demands increase, there are also opportunities. We conclude with a reflection on the potential that the blurred boundaries between living and dying hold for experimentation in long-term residential care with visions of life and health that can include death.
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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 | 0.019 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.028 | 0.037 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.003 | 0.007 |
| 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".