I-PASS Handoff Curriculum: Faculty Observation Tools
Bibliographic record
Abstract
Abstract The Faculty Observation Tools section contains a set of assessment tools designed to be used by residency programs implementing the Core Resident Workshop in order to ensure that residents acquire competency in handoff skills, as well as to reinforce the I-PASS techniques and ensure sustainment of the I-PASS Handoff Curriculum. These tools are also meant to meet the ACGME Common Program Requirements that all training programs ensure and monitor effective patient handoffs. Included are tools to assess the skills of the giver and receiver of verbal handoffs, as well as a tool to assess the quality of a printed patient handoff document. In addition, we include detailed information about how to administer and apply each item in the assessment tools. The I-PASS Handoff Curriculum: Faculty Observation Tools is one of six submissions which are part of the I-PASS Handoff Curriculum Collection, created by a group of pediatric educators, health services researchers, and hospitalists to teach a standardized approach to handoffs in inpatient settings. This collection is a comprehensive, evidence-based, and consensus-driven suite of educational materials created for a multisite study that includes the following complementary components: the Core Resident Workshop, Handoff Simulation Exercises, the Online Module, the Campaign Toolkit, and Faculty Development Resources.
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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 | 0.019 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".