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Record W2009270658 · doi:10.1155/2014/830701

Changing Morning Report: An Educational Intervention to Address Curricular Needs

2014· article· en· W2009270658 on OpenAlexafffund
Vijay Daniels, Cheryl E. Goldstein

Bibliographic record

VenueJournal of Biomedical Education · 2014
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsUniversity of Alberta Hospital
FundersUniversity of Alberta
KeywordsMorningCurriculumMedical educationSession (web analytics)Focus groupMedicinePreceptorPsychologyFamily medicinePedagogyComputer scienceInternal medicineSociology

Abstract

fetched live from OpenAlex

Morning report is a case-based teaching session common to many residency programs with varying purposes and focuses. At our institution, physicians and residents felt our Internal Medicine morning report had lost its educational focus. The purpose of this project was to improve morning report using a well-known curriculum development framework for medical education. We conducted a focus group of residents to develop and implement changes to morning report. Themes from our focus group led us to split morning report with the first 30 minutes for postgraduate year 3 (PGY-3) residents to give handover, to receive feedback on diagnosis and management, and to either discuss an interesting case or receive teaching aimed at their final certification examination. The second 30 minutes involved PGY-3 residents leading PGY-1 residents in case-based discussions with an attending physician providing feedback on the content and process of teaching. We measured success based on a follow-up survey and comments from resident evaluations before and after the change. Overall, the changes were well received by both faculty and residents; however comments revealed that the success of morning report is preceptor dependent. In summary, we have successfully implemented a split morning report model to enhance resident education with positive feedback.

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)0.003
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.897
Threshold uncertainty score0.615

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.372
Teacher spread0.359 · 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 designOther design
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

Citations6
Published2014
Admission routes2
Has abstractyes

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