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Record W1968310354 · doi:10.1038/ajg.2011.107

Observing Handoffs and Telephone Management in GI Fellowship Training

2011· article· en· W1968310354 on OpenAlexfundno aff
Reneé Williams, Roy Miler, Brijen Shah, Sita Chokhavatia, Michael A. Poles, Sondra Zabar, Colleen Gillespie, Elizabeth Weinshel

Bibliographic record

VenueThe American Journal of Gastroenterology · 2011
Typearticle
Languageen
FieldMedicine
TopicHospital Admissions and Outcomes
Canadian institutionsnot available
FundersDivision of Graduate EducationYork University
KeywordsMedicineHandoverInterpersonal communicationMedical educationTelephone callCommunication skillsPsychologyTelecommunicationsComputer science

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.213

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.039
GPT teacher head0.266
Teacher spread0.227 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2011
Admission routes1
Has abstractyes

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