Pediatric End-of-Life Simulation Workshop to Clinical Care: Lasting Implications on Clinical Practice
Bibliographic record
Abstract
Background: Simulations are an important modality for practicing high-acuity, low-frequency events. We implemented a deliberate practice simulation-based workshop to improve pediatric end-of-life care skills (PECS) competence. Purpose: To understand pediatric subspecialty fellows' perceptions about influences of a simulation-based workshop on PECS provided at the bedside several months following participation. Methods: Pediatric subspecialty fellows were recruited to voluntary focus groups during regular educational sessions six months following PECS workshop participation with aims to identify perceptions about their workshop participation and any implication on their clinical practice. Inductive qualitative content analysis of focus group interview data was performed adhering to the Standards for Reporting Qualitative Research. Results: Ten fellows participated in one of three focus groups. Researchers identified three major themes of fellow experience: burden, safe practice space, and self-efficacy. Fellows described practice implications from workshop participation, including incorporation of specific practices, improved anticipatory guidance, and increased team leader confidence. Conclusions: Targeted, deliberate simulation-based practice of PECS can help close the gap from learning to practice, contributing to provider self-efficacy and potentially improving clinical care for pediatric patients and families at end of life.
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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 | 0.014 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".