Staff opinions about the components of a good death in long-term care
Bibliographic record
Abstract
AIM: The aim of this study was to identify differences of opinion about the relative importance of different components of a good death among care providers in a long-term care home serving Canadian war veterans. METHODS: The Concept of a Good Death scale (Swartz et al, 2003), with slight adaptations to fit the long-term care context, was made available to all staff. Responses were accepted for a one-month period. FINDINGS: Survey return rate was 30.4%. There was a majority (greater than 50%) opinion that 12 of the 20 items were essential or important to a good death, and that three items were not necessary: 'that death is sudden and unexpected' (64.5%), 'that there be control of bodily functions to the end' (61.8%) and 'that there be mental alertness to the end' (55.3%). There was not a majority opinion on the five remaining items: 'that the dying period be short', 'that death occurs naturally without technical equipment', 'that the person lived until a key event', 'that the ability to communicate be present until death', and 'that death occurs during sleep'. CONCLUSIONS: Detailed analysis of survey results identified differences of opinion that could have implications for consistency and quality of care. The findings suggest ways in which the unique characteristics of the long-term care environment and population influence opinions about the components of a good 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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".