Bibliographic record
Abstract
Background and aims: Quality supportive care at a child's end of life (EOL) is important for the child, the family and the ICU team. Aims: We developed and evaluated a hybrid simulation intervention to increase skill and confidence in nurses for EOL communication with families. Methods: The intervention developed from focus groups exploring nurse's learning needs in EOL care. A simulation was designed and refined with selected interprofessional experts. Standardized patients portrayed the parents and a simulator was the child. The simulation format was 'tag in-out' and included a robust facilitated debriefing. The project received quality improvement approval. Surveys were performed at baseline and 3 months post intervention. Scaled (1-5) and open questions evaluated confidence, avoidance behaviors and emotional distress. Additional context was provided through participant feedback, facilitator notes and nurse participation in bereavement care. Results: 89 (96%) of eligible nurses completed the pre survey with 60 (73%) completing the post. The stress of peer observation approximated practice conditions. Satisfaction with the intervention was reported at 4.9/5. Median scores for confidence increased by 0.43 (p<0.001). Avoidance behavior scores did not significantly change however 55% of scores were lower on post survey. Nurse participation in bereavement follow up care was unchanged at 6 months. Scores for emotional distress were unchanged. In a review of the qualitative data, themes of 'finding the right words', 'uncertainty' and 'emotional impact' emerged. Conclusions: High emotional and physical fidelity simulation was effective in increasing confidence of established practitioners for end of life care and was highly valued by participants.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.853 | 0.777 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".