Evaluating observed and perceived experiences of operating room to paediatric critical care unit handoffs: an initial assessment to inform quality improvement
Bibliographic record
Abstract
Introduction: Transferring critically ill patients from the Operating Room (OR) to the Paediatric Critical Care Unit (PCCU) is a complex process. Unstructured handoffs and poor communication increase the risk of adverse events. This project aimed to characterize the current handoff process, identify strengths and deficiencies, and define opportunities for improving patient handover. Methods: A working group with multidisciplinary stakeholder representation was created. An audit tool was developed and used to evaluate daytime OR to PCCU handoffs. A survey was distributed electronically to all staff involved in the handoffs. Results: Audits of 50 handoffs revealed that only 71.4% of handoffs included the full perioperative team and introductions were rarely completed (14.0%). The majority (81.8%) of the Anaesthesia content was discussed consistently (>60% of the time). In contrast, over half (53.8%) of surgical elements were discussed less than 50% of the time. Sixty-two survey responses revealed team members were often absent (67.0%) or inattentive (45.0%), and handoffs lacked clarification and wrap-up (38.0%). Twenty-two percent of respondents felt information was missed and 60.0% were unsatisfied with the current handoff process. Siloed communication, need for standard pre-handoff information, and a structured handoff process were identified in survey comments. Conclusion: Audit and survey data identified multiple areas for process improvements in OR to PCCU handoffs. The combination of objective and subjective data enhanced results and informed future quality improvement efforts by engaging team members. These findings will aid in the development of a structured OR to PCCU handoff process to ensure effective and safe patient care.
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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 | 0.018 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".