The Importance of End-Of-Life Care in Nursing Home Settings is Not Diminished by a Disaster
Bibliographic record
Abstract
The emphasis in disaster situations is on preserving life, and this goal is both appropriate and laudable. There is a risk, however, that the needs of people who are dying can become lost when there is a sudden surge of people needing acute intervention. There are significant ethical considerations inherent in society's prioritization of care needs across the acute, rehabilitative, and palliative spectrum in general, let alone in a disaster situation. These ethical conundrums are not the focus of this article. Rather, we anchor our discussion on the assumption that care needs are equally valid, and our purpose is to explore the issues that impact the provision of quality end-of-life care in nursing home settings for those who require this care when a disaster occurs. Nursing home residents, in particular, are at heightened risk for experiencing negative disaster-related outcomes due to compromised physical or mental health that requires skilled nursing care. Moreover, within the already vulnerable nursing home population are many people who are receiving palliative end-of-life services when a disaster strikes. Education and training in end-of-life services for nursing home staff, disaster emergency responders, and other lay people is vital to build capacity for adapting the delivery of these services in disaster situations in the interest of equity and human rights. Given the present lack of guidance in the literature as to what end-of-life care looks like when adapted to the context of disaster response, there is also a pressing need for research to inform this discussion. The purpose of this article is to draw attention to this topic and highlight some of the critical issues, gaps, and opportunities.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".