75 The association between patient ethnicity and family satisfaction with the quality and provision of end-of-life care
Notice bibliographique
Résumé
Objectives Among patients who have died in Ontario, Canada, recent immigrants and ethnic minorities were more likely to receive aggressive treatment in the last six months of life and more likely to die in intensive care units (ICU) in comparison to other patients. Insight into the end-of-life care experiences of patients from diverse ethnocultural backgrounds can help identify high priority areas for quality improvement. This observational survey-based study aimed to evaluate satisfaction with the quality of end-of-life care for patients of diverse ethnocultural backgrounds from the perspective of bereaved family members. The primary aim was to evaluate the association between satisfaction with quality of end-of-life care and patient ethnocultural characteristics. The secondary aim was to identify high priority areas for quality-of-care improvement. Method The End-of-Life Satisfaction Survey (a validated 52-item tool) was used to measure satisfaction with the quality of in-patient end-of-life care from the perspective of next-of-kin of recently deceased patients at Sunnybrook Health Sciences Centre in Toronto, Canada (between March 2012 to May 2019). The primary outcome was the global rating of satisfaction score which was measured on a 10-point Likert scale. Potential explanatory variables were selected a priori based on clinical relevance to the research question and prior research. Associations with patient ethnicity, patient religion, level of religiosity/spirituality, language/communication barriers, and location of death were assessed using univariable and multivariable modified Poisson regression. Secondary outcomes included differences in satisfaction and rates of dying in ICU among patient population subgroups, and identification of high priority areas for quality-of-care improvement. Results are described in terms of relative risk (RR) with a significance threshold level of α=0.05. Descriptive statistics included counts and proportions for categorical variables and means (standard deviations) and medians (interquartile ranges) for continuous variables. Results There were 1,543 respondents. Patient ethnicities included Caucasian (68.2%), Mediterranean (10.5%), East Asian (7.6%), South Asian (3.5%), Southeast Asian (2.1%) and Middle Eastern (2.0%); religious affiliations included Christianity (66.6%), Judaism (12.3%) and Islam (2.1%), among others. Location of death was most commonly in ICU (38.4%), hospital wards (37.0%) or long-term care (20.0%). The mean(SD) rating of satisfaction score was 8.30(2.09) of 10. Satisfaction with the quality of end-of-life care was higher among patients dying in ICU versus other locations (adjusted RR 1.51, 95%CI 1.05-2.19, p=0.028), lower among those who experienced language/communication barriers (adjusted RR 0.49 95%CI 0.23-1.06, p=0.069), and lower for Muslim patients versus other religious affiliations (adjusted RR 0.46, 95%CI 0.21-1.02, p=0.056). High priority areas for quality-of-care improvement included: communication and information giving; illness management; and healthcare provider characteristics such as emotional support, doctor availability and time spent with patient/family. Conclusions Satisfaction with the quality of end-of-life care was higher among patients dying in ICU compared to other hospital units and lower among Muslim patients or when there were language/communication barriers between patients, families and the healthcare team. These findings underscore the need to identify and remedy potential disparities in care delivery and/or ethnocultural expectations for care that may contribute to lower satisfaction at the end of life. Complementary qualitative research designs should seek to capture the patient care experience of patients and families from specific ethnocultural backgrounds. Presenter/Submitter Biography* Provide a 3-4 sentence biography exactly the way you would like it read for your introduction and loaded onto conference app. Your biography is limited to 150 words. Please read your biography aloud once it is written to ensure it can be effortlessly delivered by the session monitor. Ayah Nayfeh is a PhD candidate in Health Services Research at the University of Toronto’s Institute of Health Policy, Management and Evaluation (IHPME). Her research background and interest in health equity and implementation science led to her academic work around improving end-of-life care for recently immigrated and ethnic minority patients in Canada. Ayah has previously held positions at Healthcare Excellence Canada; the Canadian Institutes of Health Research; and at Canada’s International Development Research Centre. She received a MSc in Interdisciplinary Health Sciences from the University of Ottawa and a BHSc (Hons) in Health Sciences with a Specialization in Biology from the University of Western Ontario.
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 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».