I-PASS Handoff Curriculum: Computer Module
Bibliographic record
Abstract
Abstract The I-PASS Computer Module provides a unique platform for asynchronous, individualized learning of key elements of the I-PASS curriculum. It features embedded knowledge questions which provide an evaluation of learners' understanding of each of the major I-PASS concepts. In addition, individual subtopics may be accessed separately if one wishes to refresh knowledge of certain components of the I-PASS Curriculum. Video elements, supplemental printable documents, and test your knowledge questions are integrated into the module. 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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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".