I-PASS Handoff Curriculum: Core Resident Workshop
Bibliographic record
Abstract
Abstract The Core Resident Workshop is the centerpiece of the I-PASS Curriculum Collection. The workshop features a 2-hour didactic and interactive session devoted to team training in structured communication techniques, and to the teaching of a standardized approach to the handoff process (including the integration of oral and written handoff components). Key structured team communication techniques and the I-PASS mnemonic are taught in detail and reinforced with the use of trigger videos and large group discussion. Ideally this 2-hour session is followed by the 1-hour handoff simulation exercises which features small group interactive role-plays in which participants gain hands-on experience and practice the I-PASS handoff technique. This workshop is also suitable for a faculty educational retreat. In brief, we found in a detailed review of 10,740 patient admissions that a 23% reduction in medical errors and a 30% reduction in injuries due to medical errors (preventable adverse events) occurred following implementation of the I-PASS Handoff Bundle in nine academic medical centers. In direct observation of thousands of hours of resident workflow (time motion analysis) before and after implementation of the program, conducting handoffs using the I-PASS method was found to require no more time per handoff, and resident workflow throughout the shift was likewise unchanged, including no change in the amount of time spent at the computer or in direct 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".