Real World Implementation of a Standardized Handover Program (I-PASS) on a Pediatrics Clinical Teaching Unit
Bibliographic record
Abstract
Introduction: A standardized handover curriculum (I-PASS) has been shown to reduce preventable adverse events in a large multicenter study. We aimed to study the real world impact of the implementation of this curriculum on handover quality, duration, and critical care calls.Methods: A prospective intervention study was conducted. We implemented the I-PASS curriculum via faculty education sessions and resident workshops. Resident handover was video-recorded and written lists were collected for 2 weeks pre- and postintervention. We examined the inclusion of key elements on handover lists pre- and postintervention using logistic regression models accounting for multiple handovers per patient. Duration of handover was compared using a linear regression model adjusting for number of patients. Qualitative content analysis was used to describe observable differences in video recordings and written critical care call records.Results: A total of 1275 handovers were included, comprising 364 inpatients. There was a significant increase (P < .05) in 7 of 11 key elements (including illness severity, action items, and contingency plans) and a significant decrease in written physical examination findings postintervention. No significant change was found in handover duration. Video analysis revealed observable differences in handover structure, consistency, and detail. There was no significant difference in the number of critical care calls, although postintervention all patients requiring critical care calls were correctly identified as requiring close monitoring during handover.Conclusions: Handover training resulted in consistent inclusion of key elements without significant increase in handover duration. Qualitative analyses suggest appropriate identification and response to severely ill patients using the I-PASS model.
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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 | 0.004 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".