OP-18 Understanding peaceful dying among Canada’s elderly
Bibliographic record
Abstract
Background Death is a universal phenomenon that is an intrinsic part of the human experience and a cornerstone of clinical science, yet little is known about how Canadians experience death.1-3 We examined novel data from the Canadian Longitudinal Study on Aging to describe peace with dying among older Canadians and examine correlates.4 Methods We conducted a secondary analysis of decedent interview data from the Canadian Longitudinal Study on Aging (CLSA) in Canada. Next of kin and proxies of deceased CLSA participants were interviewed and reported on the End-of-Life (EoL) experiences of participants who died between January 2012 to March 2022. We examined EoL characteristics, including the location of death, cause of death, arrangements for health care decision making, and arrangements for end-of-life care decision making and their association with dying peacefully. Regression methods identified the association between demographic and EoL characteristics in experiencing peace with dying. Results There were 3,672 total deceased participants at the CLSA and 1,287 had completed a decedent questionnaire. Sampled decedents (55.3%) were 75 years old or older at death, 62.0% were male, 62.7% were married, and 39.7% died of cancer. Next of kins reported that 66.0% of the deceased experienced peace with dying, 7.0% were ‘somewhat’ at peace with dying, and 17% did not experience peace with dying. A peaceful death was more likely if the deceased was older (75+; OR 1.55; CI 1.04–2.30), widowed (OR 1.53; CI 1.12–2.10), died of cancer (OR 1.71; CI 1.27–2.30), died in hospice/palliative care (OR 1.67; CI 1.19–2.37) and having an appointed EoL decision making power of attorney (OR 1.80; CI 1.39–2.33). Conclusions Many older Canadian do not experience peace with dying which underscores the greater public need and demand for health system focus on improving the quality of death.5 6 Our findings support the presumption of effectiveness for end-of-life programs as well as programs that include advanced planning regarding wishes and decision making as potentially modifiable factors to support quality of death. A person’s experience with close family member death, predictability of course of illness, and strength of close social bonds are less modifiable factors that can support how end of life programs are designed and targeted. References Ko E, Kwak J, Nelson-Becker H. What constitutes a good and bad death?: Perspectives of homeless older adults. Death Studies. 2015;39:422–32. Georges JJ, Onwuteaka-Philipsen BD, van der Heide A, van der Wal G, van der Maas PJ. Symptoms, treatment and ‘dying peacefully’ in terminally ill cancer patients: a prospective study. Support Care Cancer. 2005;13:160–8. Teno JM, FreedmanV. A., Kasper, J. D., Gozalo P., Mor, V. Is care for the dying improving in the United States? Journal of palliative medicine. 2015;18:662–6. Van Soest-Poortvliet MC, van der Steen JT, Zimmerman S, Cohen LW, Munn J, Achterberg WP, et al. Measuring the quality of dying and quality of care when dying in long-term care settings: a qualitative content analysis of available instruments. J Pain Symptom Manage. 2011;42:852–63. De Roo ML, van der Steen, J. T., Galindo Garre, F., Van Den Noortgate, N., Onwuteaka-Philipsen, B. D., Deliens, L., EURO IMPACT.When do people with dementia die peacefully? An analysis of data collected prospectively in long-term care settings.Palliative Medicine. 2014;28:210–9. Diaconu V, Ouellette, N., Camarda, C. G., Bourbeau, R.Insight on ‘typical’longevity: An analysis of the modal lifespan by leading causes of death in Canada. Demographic Research. 2016; 35:471–504.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".