P-4 Bridging the gap: understanding the divide between those who consider and those who receive medical assistance in dying
Notice bibliographique
Résumé
Introduction Medical Assistance in Dying (MAiD) allows eligible individuals to access medical interventions to end their lives when facing an advanced, irreversible condition accompanied by unbearable suffering.1 2 Many individuals who seek a MAiD may not receive MAiD, due to the lengthy and complex nature of the MAiD process.3 We aimed to understand the differences between participants who considered MAiD but did not undergo the procedure and those who underwent a MAiD. 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 about end-of-life characteristics and MAiD considerations for participants who died between June 6, 2016, and March 15, 2022. We examined clinical and demographic characteristics and their association with considering MAiD compared to receiving MAiD. We conducted a descriptive analysis comparing non-MAiD deaths to MAiD-related deaths. Regression methods identified the association between demographic and EoL characteristics factors with consideration and reception of MAiD. Results There was a total of 981 deceased participants with a completed decedent interview. Approximately 25.4% considered MAiD and 6.7% experienced MAiD. In both groups, most participants were male, married, and died of cancer. Considering MAiD was more likely if individuals died in hospice or palliative care (OR 1.73; CI 1.12–2.67), had health care or end-of-life arrangements (OR 1.75; CI 1.15–2.76), and experienced peace with dying (OR 1.87; CI 1.23–2.92). For those who had a MAiD, they were less likely receive palliative care, but had a better overall quality of death and dying experience. Individuals considering MAiD reported dying in place (64.7 vs 56.3; SD: 0.75) and peace with dying (78.3 vs 63.7; SD 0.77) more frequently than those who did not consider MAiD. Discussion Given that more than a quarter of older adults are considering MAiD, honest and informed conversations between health care providers and patients regarding MAiD need to become a part of the EoL care planning process.4 Palliative care settings may offer effective symptom management and psychosocial support that may alleviate the need for MAiD. Considering MAiD as an end-of-life care pathway, even if not received, enhances the overall quality of the dying experience, by providing autonomy during the end-of-life decision-making process contributing to a positive death experience.5 6 References Downar J, Fowler RA, Halko R, Huyer LD, Hill AD, Gibson JL. Early experience with medical assistance in dying in ontario, Canada: a cohort study. CMAJ. 2020;192.E173-E81. An act to amend the criminal code and to make related amendments to other acts (Medical Assistance In Dying) (S.C. 2016 c. Martin S. A good death: Making the most of our final choices. Toronto CHC. Mathews JJ, Hausner D, Avery J, Hannon B, Zimmermann C, Al-Awamer A. Impact of medical assistance in dying on palliative care: a qualitative study. Palliat Med. 2021;35:447–54. (https://www.canada.ca/en/health-canada/services/publications/health-system-services/annual-report-medical-assistance-dying-2022.html) SCFaroMAiDiC. https://www.canada.ca/en/health-canada/services/publications/health-system-services/annual-report-medical-assistance-dying-2022.html. SCFaroMAiDiCN-. Canada S. Medical assistance in dying, 2021. (https://www150.statcan.gc.ca/n1/daily-quotidien/230213/dq230213c-eng.htm). 2023.
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 | 0,007 | 0,021 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,007 | 0,012 |
| Communication savante | 0,007 | 0,012 |
| Science ouverte | 0,002 | 0,007 |
| Intégrité de la recherche | 0,003 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,001 |
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 ».