Improving paediatric clinical teaching unit handover: A quality improvement project
Bibliographic record
Abstract
Background: Handover is an integral part of patient care and is dependent on effective communication between physicians. Poor quality handover can lead to patient harm, with up to 75% of patients in whom there are handover failures sustaining preventable adverse events. Paediatric Clinical Teaching Unit (CTU) Morbidity and Mortality rounds identified multiple handover-related adverse events. We therefore undertook a quality improvement project to reduce handover-related adverse events and improve participant satisfaction with handover. Methods: This project was carried out in two phases at an academic tertiary care paediatric hospital in London, Ontario. Phase I involved recording any adverse events that occurred overnight. A root cause analysis with paediatric residents identified the key contributors to poor handover. Phase II implemented strategies aimed at addressing these contributors. CTU handovers were then observed using the same questionnaires as Phase I. Following Phase II, a questionnaire was sent to all paediatric residents to evaluate their perceived changes in handover. Interventions: Designating a handover room, collaborating with nurses to reduce pages during handover, changing pager messages to minimize non-emergent pages during handover, creating a handover template, and providing formal teaching to medical students and residents. Results: Implementation of the interventions resulted in a significant decrease in interruptions and background noise. There was a trend toward a reduction in adverse events from 13 in Phase I to 7 in Phase II. All residents felt that handover improved, with 16.7% stating minimal improvement, 61.1% stating some improvement, and 22.2% stating good improvement. Conclusions: Results show that the simple strategies implemented improved resident handover.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.029 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".