Peaceful dying among Canada’s elderly: An analysis of the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
INTRODUCTION: Death is universal, yet relatively little is known about how Canadians experience their death. Using novel decedent interview data from the Canadian Longitudinal Study on Aging we describe the prevalence and characteristics of peace with dying among older Canadians. METHODS: We conducted a secondary analysis of decedent interview data from the Canadian Longitudinal Study on Aging. Proxies of deceased Canadian Longitudinal Study on Aging participants reported on participants' end-of-life experiences between January 2012 to March 2022. We examined end-of-life characteristics and their association with proxy reports of experiencing peace with dying. We conducted regression analysis to explore the association between demographic and end-of-life characteristics and experiencing peace with dying. RESULTS: Of 3,672 deceased participants, 1,287 (35.0%) had a completed decedent questionnaire and were included in the analysis. Respondents reported that two-thirds (66.0%) of the deceased experienced peace with dying and 17% did not experience peace with dying. The unadjusted odds of experiencing peace with dying were higher for those with an appointed power of attorney (OR 1.80; CI 1.39-2.33), those who died of cancer (OR 1.71; CI 1.27-2.30), those in hospice/receiving palliative care (OR 1.67; CI 1.19-2.37), individuals older than 75 years (OR 1.55; CI 1.04-2.30), or widowed (OR 1.53; CI 1.12-2.10). Widowhood (OR 1.51; CI 1.01-2.29), having an end-of-life SDM (OR 1.58; CI 1.14-2.17), and dying of cancer (OR 1.67; CI 1.19-2.23) increased the adjusted odds of dying with peace. CONCLUSIONS: Close to 1 in 5 older Canadians may not experience peace with dying, which supports greater focus on improving the end-of-life care. Our findings suggest that advanced planning may enhance the experience of a peaceful death in Canada.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.002 |
| 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".