Dying at home – is it better: A narrative appraisal of the state of thescience
Bibliographic record
Abstract
BACKGROUND: Achieving home care and home death is increasingly used as an outcome measure of palliative care services. AIM: To appraise the state of the science on dying at home. METHODS: Appraisal and narrative review developed from a plenary presentation at the European Association for Palliative Care (EAPC) 2012 meeting examining the research on variations and trends in place of death, factors associated with dying in the preferred place, presenting evidence on outcomes for those dying at home and suggesting future research questions. RESULTS: Meeting patients' preferences and creating home-like environments has been a major concern for hospice and palliative care since its inception. During the 20(th) century, in many countries, hospital deaths increased and home deaths reduced. Despite the fact that this trend has been halted or reversed in some countries (notably the United States, Canada and, more recently, the United Kingdom) in the last 5-20 years, a home death is still a distant reality for the majority, even though evidence shows it is the most commonly preferred place to die. Epidemiological studies identified factors associated with home death, including affluence, patients' preferences, provision of home care and extended family support. Evidence about the benefits of home care is conflicting, but recent data suggest that holistic well-being may be greater at home. IMPLICATIONS: We call for further analyses of variations in place of care and place of death and robust studies on how patients and families formulate and change preferences over time. Regular monitoring of outcomes, quality and costs of palliative home care is urged.
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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 | 0.036 | 0.145 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".