Factors Affecting Family Satisfaction with Inpatient End-of-Life Care
Bibliographic record
Abstract
BACKGROUND: Little data exists addressing satisfaction with end-of-life care among hospitalized patients, as they and their family members are systematically excluded from routine satisfaction surveys. It is imperative that we closely examine patient and institution factors associated with quality end-of-life care and determine high-priority target areas for quality improvement. METHODS: Between September 1, 2010 and January 1, 2012 the Canadian Health care Evaluation Project (CANHELP) Bereavement Questionnaire was mailed to the next-of-kin of recently deceased inpatients to seek factors associated with satisfaction with end-of-life care. The primary outcome was the global rating of satisfaction. Secondary outcomes included rates of actual versus preferred location of death, associations between demographic factors and global satisfaction, and identification of targets for quality improvement. RESULTS: Response rate was 33% among 275 valid addresses. Overall, 67.4% of respondents were very or completely satisfied with the overall quality of care their relative received. However, 71.4% of respondents who thought their relative did not die in their preferred location favoured an out-of-hospital location of death. A common location of death was the intensive care unit (45.7%); however, this was not the preferred location of death for 47.6% of such patients. Multivariate Poisson regression analysis showed respondents who believed their relative died in their preferred location were 1.7 times more likely to be satisfied with the end-of-life care that was provided (p = 0.001). Items identified as high-priority targets for improvement included: relationships with, and characteristics of health care professionals; illness management; communication; and end-of-life decision-making. INTERPRETATION: Nearly three-quarters of recently deceased inpatients would have preferred an out-of-hospital death. Intensive care units were a common, but not preferred, location of in-hospital deaths. Family satisfaction with end-of-life care was strongly associated with their relative dying in their preferred location. Improved communication regarding end-of-life care preferences should be a high-priority quality improvement target.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".