Defining priorities for improving end-of-life care in Canada
Bibliographic record
Abstract
BACKGROUND: High-quality end-of-life care should be the right of every Canadian. The objective of this study was to identify aspects of end-of-life care that are high in priority as targets for improvement using feedback elicited from patients and their families. METHODS: We conducted a multicentre, cross-sectional survey involving patients with advanced, life-limiting illnesses and their family caregivers. We administered the Canadian Health Care Evaluation Project (CANHELP) questionnaire along with a global rating question to measure satisfaction with end-of-life care. We derived the relative importance of individual questions on the CANHELP questionnaire from their association with a global rating of satisfaction, as determined using Pearson correlation coefficients. To determine high-priority issues, we identified questions that had scores indicating high importance and low satisfaction. RESULTS: We approached 471 patients and 255 family members, of whom 363 patients and 193 family members participated, with response rates of 77% for patients and 76% for families. From the perspective of patients, high-priority areas needing improvement were related to feelings of peace, to assessment and treatment of emotional problems, to physician availability and to satisfaction that the physician took a personal interest in them, communicated clearly and consistently, and listened. From the perspective of family members, similar areas were identified as high in priority, along with the additional areas of timely information about the patient's condition and discussions with the doctor about final location of care and use of end-of-life technology. INTERPRETATION: End-of-life care in Canada may be improved for patients and their families by providing better psychological and spiritual support, better planning of care and enhanced relationships with physicians, especially in aspects related to communication and decision-making.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".