75 The association between patient ethnicity and family satisfaction with the quality and provision of end-of-life care
Bibliographic record
Abstract
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.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".