Reasons for requesting medical assistance in dying.
Notice bibliographique
Résumé
OBJECTIVE: To review the charts of people who requested medical assistance in dying (MAID) to examine their reasons for the request. DESIGN: Retrospective chart survey. SETTING: British Columbia. PARTICIPANTS: Patients who requested an assisted death and were assessed by 1 of 6 physicians in British Columbia during 2016. MAIN OUTCOME MEASURES: Patients' diagnoses and reasons for requesting MAID. RESULTS: Data were collected from 250 assessments for MAID: 112 of the patients had assisted deaths, 11 had natural deaths, 35 were assessed as not eligible for MAID, and most of the rest were not ready. For people who had assisted deaths, disease-related symptoms were given as the first or second most important reason for requesting assisted death by 67 people (59.8%), while 59 (52.7%) gave loss of autonomy, 55 (49.1%) gave loss of ability to enjoy activities, and 27 (24.1%) gave fear of future suffering. People who were assessed as eligible but who had not received assisted deaths were more likely to list fear of future suffering (33.7% vs 7.1%) and less likely to list disease-related symptoms (17.4% vs 40.2%) than those who received MAID were. There was a difference in reasons for MAID given by people with different diagnoses; disease-related symptoms were given as the most important reason by 39.0% of patients with malignancies, 6.8% of patients with neurological diseases, and 28.9% of patients with end-organ failure. Loss of autonomy was given as the most important reason by 16.0% of patients with malignancies, 36.4% of patients with neurological diseases, and 23.7% of patients with end-organ failure. CONCLUSION: This study shows that the reasons patients give for requesting an assisted death are similar to those reported in other jurisdictions with similar laws, but in different proportions. Loss of autonomy and loss of ability to enjoy activities were less common reasons among patients in this study compared with other jurisdictions. This might be related to the method of data collection, as in this study, the patients' reasons were recorded by physicians.
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 | 0,001 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».