A QI initiative to enhance nurses comfort level in providing EOL care on a general internal medicine ward
Bibliographic record
Abstract
Background and objective: Ideally, nurses should have acquired the knowledge and skills to provide not only pain and symptom management, but emotional support to dying patients and their families. In actual fact, nurses often feel under-educated, under-skilled, inexperienced and uncomfortable in providing comprehensive end of life care. A standardized protocol for assessing imminently dying patients’ symptoms and psychosocial needs was implemented at a tertiary academic hospital. The aim of this quality improvement initiative was to enhance the education and training of nurses on one acute care ward, around the implementation of the Comfort Measures Order Set for end of life care, specifically focusing on the provision of emotional support for dying patients and their families.Methods: Education sessions were offered to the nurses on one acute care ward as the study intervention, and the initial phase of one PDSA cycle. Descriptive statistics were used to analyze questionnaire responses; content analysis was used in reviewing the qualitative data.Results: Pre-intervention, over 70% of nurses did not feel comfortable providing emotional support to dying patients for whom the Comfort Mesaures Order Set was initiated. Post-intervention, nurses reported being more comfortable and knowledgeble.Conclusions: The goal of comfort care at end of life requires the skilled use of the Comfort Measures Order Set. The Advance Practice Nurse role, as part of the Palliative Care Consult Team, is specialized in providing emotional support to patients on comfort measures through research, theory development, education, practice, collaboration, and leadership across the institution.
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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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".