Observing Handoffs and Telephone Management in GI Fellowship Training
Bibliographic record
Abstract
OBJECTIVES: Gastroenterology (GI) training programs are mandated to teach fellows interpersonal communication and professionalism as basic competencies. We sought to assess important skill sets used by our fellows but not formally observed or measured: handoffs, telephone management, and note writing. We designed an Observed Standardized Clinical Examination (OSCE) form and provided the faculty with checklists to rate fellows' performance on specific criteria. METHODS: We created two new scenarios: a handoff between a tired overnight senior fellow on call and a more junior fellow, and a telephone management case of an ulcerative colitis flare. Fellows wrote a progress notes documenting the encounters. To add educational value, we gave the participants references about handoff communication. Four OSCE stations-handoff communication, telephone management, informed consent, and delivering bad news-were completed by fellows and observed by faculty. RESULTS: Eight faculty members and eight fellows from four GI training programs participated. All the fellows agreed that handoffs can be important learning opportunities and can be improved if they are structured, and that handoff skills can improve with practice. CONCLUSIONS: OSCEs can serve as practicums for assessing complex skill sets such as handoff communication and telephone management.
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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 | 0.006 | 0.031 |
| 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 | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".