Evaluating satisfaction with the quality and provision of end-of-life care for patients from diverse ethnocultural backgrounds
Bibliographic record
Abstract
BACKGROUND: Recently immigrated and ethnic minority patients in Ontario, Canada are more likely to receive aggressive life-prolonging treatment at the end of life in comparison to other patients. To explore this finding further, this survey-based observational study aimed to evaluate satisfaction with the quality of end-of-life care for patients from diverse ethnocultural backgrounds. METHODS: The End-of-Life Satisfaction Survey was used to measure satisfaction with the quality of inpatient end-of-life care from the perspective of next-of-kin of recently deceased patients at Sunnybrook Health Sciences Centre in Toronto, Ontario (between March 2012 to May 2019). The primary outcome was the global rating of satisfaction. 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 intensive care units (ICU) among patient population subgroups, and identification of high priority areas for quality-of-care improvement. 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. After adjusting for other covariates, satisfaction with quality of end-of-life care was higher among patients dying in ICU versus other locations (relative risk [RR] 1.51, 95%CI 1.05-2.19, p=0.028), lower among those who experienced language/communication barriers (RR 0.49 95%CI 0.23-1.06, p=0.069), and lower for Muslim patients versus other religious affiliations (RR 0.46, 95%CI 0.21-1.02, p=0.056). Survey items identified as highest 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. CONCLUSION: Satisfaction with quality-of-care at the end of life was higher among patients dying in ICU and lower among Muslim patients or when there were communication barriers between families and healthcare providers. These findings highlight the importance of measuring and improving end-of-life care across the ethnocultural spectrum.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".