How to define and quantify a bad death in palliative home care? Across-sectional and exploratory study using Canadian interRAI data
Bibliographic record
Abstract
BACKGROUND: Dying is a complex process comprised of physical, social, cultural, spiritual, environmental, and interpersonal relationship factors that contribute to both good and bad death experiences. Bad deaths have historically been explored with a qualitative lens. This study aimed to identify key indicators of a bad death and examine predictors for each indicator using population-level data. METHODS: This cross-sectional study analyzed routinely collected clinical and sociodemographic data using the Resident Assessment Instrument for Home Care (RAI-HC) between April 2007 and March 2020. 16,586 home care clients aged 18 years and older who died and had an assessment completed within 30 days of their death were included. Four indicators of a bad death were examined: self-reported loneliness, severe depressive symptoms, daily pain that is horrible or excruciating, and pain that is severe/excruciating and uncontrolled by medications. These indicators were interRAI specific variables that captured common bad death constructs in the existing literature. The study sample was separated into groups based on these four indicators and each individual could populate more than one group. Chi-square analyses were used to examine the relationship between potential risk factors and each bad death indicator. RESULTS: Of the total sample, 50.9% were 85 + years of age, and 54.7% were female. The prevalence of experiencing at least one of the bad death indicators was 33.5%. Each indicator significantly increased the likelihood of experiencing one of the other indicators with the ORs ranging from 1.70 to 3.26. Other important predictors that increased the odds of experiencing each bad death indicator included: any psychiatric diagnoses (OR range: 1.29-1.89), experiencing conflict with family or friends (OR range: 1.21-3.40), and a decline in social interaction which was distressing to the person (OR range: 2.06-3.70). CONCLUSIONS: These four bad death indicators were common among community-dwelling adults. This study found that there was an interconnectedness between the bad death indicators. Clinically, the relationship between these indicators means that addressing one aspect of a bad death may positively influence the others. Early identification of these issues, along with client and family collaboration, can aid in optimizing the likelihood 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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".