Improving the comfort of nurses caring for stroke patients at the end of life
Bibliographic record
Abstract
BACKGROUND: End-of-life care of stroke patients is an important aspect of stroke care. It has been previously reported that nurses express discomfort caring for patients at the end of life or caring for patients who have suffered severe strokes. Nurses at our centre expressed similar discomfort. AIM: To improve the comfort of nurses caring for patients at the end of life after stroke. DESIGN: Nurses were asked to rate their comfort with treating patients at the end of life using the Stroke End-of-Life Care Comfort Scale before and after attending an education session. The education sessions included the presentation of a checklist for suggested orders for end-of-life care. SETTING/PARTICIPANTS: The project was conducted with the neurosciences nurses at a tertiary care hospital. 54 out of a possible 122 nurses attended an education session. RESULTS: There was a significant improvement in the Stroke End-of-Life Care Comfort Scale score (p=0.00004) 3-4 weeks after the education session compared to the score before the session. CONCLUSIONS: The combination of focused education sessions and an order checklist can significantly improve the comfort of nurses caring for patients at the end-of-life after stroke.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".