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Record W4387830539 · doi:10.1177/23821205231208790

Classroom-Based Learning in an Academic Obstetrics and Gynecology Residency Training Program

2023· article· en· W4387830539 on OpenAlexaff
Riki Dayan, Tien T T Quach, Sheila With, Jagdeep Ubhi, Hanna Ezzat, Luke Y. C. Chen

Bibliographic record

VenueJournal of Medical Education and Curricular Development · 2023
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsUniversity of British Columbia
FundersDepartment of Obstetrics and Gynecology, University of Wisconsin-Madison
KeywordsMentorshipFormative assessmentMedical educationObstetrics and gynaecologySpecialtyMedicinePsychologyFamily medicinePedagogy

Abstract

fetched live from OpenAlex

Objectives: Classroom-based learning such as academic half days (AHDs) are complementary to workplace learning in postgraduate medical education. This study examined three research questions: the purpose of AHDs, elements of an effective AHD, and factors that make AHD sustainable. Methods: = 7) and the program administrator was interviewed in 2018. The themes in each research question were identified by modified inductive analysis. Results: Residents expressed that the purposes of AHD included: providing organization and an overview for their knowledge acquisition; preparation for their Royal College specialty exam; and to provide a venue for peer support and mentorship. Elements of an effective AHD include the repetition of key concepts; formative assessments such as quizzes, a suitable balance of faculty input and resident active participation, and protection from clinical duty during AHD. Regarding the sustainability of AHD, themes included: addressing barriers to faculty participation, providing administrative support for logistical needs, and providing feedback to faculty. Conclusions: This work provides important insights into the purpose, effectiveness, and sustainability of AHDs for those who design and implement classroom learning for residents.

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)0.020
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.898
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.397
Teacher spread0.358 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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