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Record W2772147789 · doi:10.1186/s13104-017-3027-5

Junior Rounds: an educational initiative to improve role transitions for junior residents

2017· article· en· W2772147789 on OpenAlexaff
Richard Dunbar‐Yaffe, Wayne L. Gold, Peter E. Wu

Bibliographic record

VenueBMC Research Notes · 2017
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsToronto General HospitalUniversity of Toronto
Fundersnot available
KeywordsPreparednessCurriculumSession (web analytics)Medical educationMedicineMedical schoolFamily medicinePsychologyPedagogyComputer science

Abstract

fetched live from OpenAlex

OBJECTIVE: At our institution, Morning Report focuses mostly on diagnostic reasoning. This makes it a challenge for first-year residents to learn to manage common on-call emergencies, such as hyperkalemia. We sought to improve their preparedness for the transitions they would encounter: from medical student to physician at the beginning of the academic year, and from junior resident to senior resident toward the end. In response to feedback, we developed the Junior Rounds curriculum: a weekly session focused on the approach to commonly encountered on-call emergencies and internal medicine referrals. Anonymous surveys were sent to trainees, and iterative analysis of monthly feedback led to changes to Junior Rounds. RESULTS: Junior Rounds was implemented from August 2015 to June 2016. Thirty-nine of 92 possible respondents (44%) completed surveys in that period. Most respondents agreed that Junior Rounds met their educational needs, was presented at an appropriate level, and was more important to their learning than other available educational activities. Our experience demonstrates that dedicated time for level-specific learning aimed to support the transitions of junior residents can be successfully achieved. Iterative adjustment to these rounds based on feedback allowed for evolution of the curriculum to meet the changing priorities of junior learners.

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)0.002
metaresearch head score (Gemma)0.030
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.798
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.030
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.240
GPT teacher head0.538
Teacher spread0.298 · 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.

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

Citations5
Published2017
Admission routes1
Has abstractyes

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