Improving end-of-life care through quality improvement
Bibliographic record
Abstract
Although end of life (EoL) care has been identified as an area for quality improvement in hospitals, the quality of care Canadian patients receive at the end of life is not well-evidenced. National statistics indicate that Canadians would prefer to die at home, yet more than 50% die in acute care hospital settings. Busy and often highly specialised acute care units may be perceived as a distressing place of death for both patients and their families. Furthermore, many clinicians are not trained in diagnosing imminent dying, managing symptoms at the end of life or supporting dying patients and their families. As such, to improve the experience of EoL care, a corporate, institution-wide strategy entitled the Quality Dying Initiative was introduced and implemented across a tertiary care academic teaching hospital. A primary focus of this initiative was the implementation of a comprehensive Comfort Measures Strategy. This strategy involved the development of an evidence-based order set, which included elements of symptom assessment and management, patient and family education, and spiritual and emotional support. Staff education and mentoring was also a critical element of the larger Comfort Measures Strategy, as well as an evaluative component.
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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 | 0.034 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".