Where would Canadians prefer to die? Variation by situational severity, support for family obligations, and age in a national study
Bibliographic record
Abstract
BACKGROUND: Death at home has been identified as a key quality indicator for Canadian health care systems and is often assumed to reflect the wishes of the entire Canadian public. Although research in other countries has begun to question this assumption, there is a dearth of rigorous evidence of a national scope in Canada. This study addresses this gap and extends it by exploring three factors that moderate preferences for setting of death: situational severity (entailing both symptoms and supports), perceptions of family obligation, and respondent age. METHODS: Two thousand five hundred adult respondents from the general population were recruited using online panels between August 2019 and January 2020. The online survey included three vignettes, representing distinct dying scenarios which increased in severity based on symptom management alongside availability of formal and informal support. Following each vignette respondents rated their preference for each setting of death (home, acute/intensive care, palliative care unit, nursing home) for that scenario. They also provided sociodemographic information and completed a measure of beliefs about family obligations for end-of-life care. RESULTS: Home was the clearly preferred setting only for respondents in the mild severity scenario. As the dying scenario worsened, preferences fell for home death and increased for the other options, such that in the severe scenario, most respondents preferred a palliative care or hospice setting. This pattern was particularly distinct among respondents who also were less supportive of family obligation norms, and for adults 65 years of age and older. CONCLUSIONS: Home is not universally the preferred setting for dying. The public, especially older persons and those expressing lower expectations of families in general, express greater preference for palliative care settings in situations where they might have less family or formal supports accompanied by more severe and uncontrolled symptoms. Findings suggest a) the need for public policy and health system quality indicators to reflect the nuances of public preferences, b) the need for adequate investment in hospices and palliative care settings, and c) continuing efforts to ensure that home-based formal services are available to help people manage symptoms and meet their preferences for setting of death.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".