OP-18 Understanding peaceful dying among Canada’s elderly
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,008 | 0,004 |
| Communication savante | 0,004 | 0,002 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».