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Record W2564652992 · doi:10.1186/s12909-016-0835-6

Pelvic and breast examination skills curricula in United States medical schools: a survey of obstetrics and gynecology clerkship directors

2016· article· en· W2564652992 on OpenAlexaff
Lorraine Dugoff, Archana Pradhan, Petra M. Casey, John L. Dalrymple, Jodi Abbott, Samantha D. Buery-Joyner, Alice Chuang, Amie J. Cullimore, David A. Forstein, Brittany Star Hampton, J Kaczmarczyk, Nadine T. Katz, Francis S. Nuthalapaty, Sarah M. Page-Ramsey, Abigail Wolf, Nancy A. Hueppchen

Bibliographic record

VenueBMC Medical Education · 2016
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineCurriculumObstetrics and gynaecologyPelvic examinationBreast examinationMedical educationAccreditationFamily medicineObstetricsGynecologyPsychologyBreast cancerMammographyInternal medicinePregnancyPedagogy

Abstract

fetched live from OpenAlex

BACKGROUND: Learning to perform pelvic and breast examinations produces anxiety for many medical students. Clerkship directors have long sought strategies to help students become comfortable with the sensitive nature of these examinations. Incorporating standardized patients, simulation and gynecologic teaching associates (GTAs) are approaches gaining widespread use. However, there is a paucity of literature guiding optimal approach and timing. Our primary objective was to survey obstetrics and gynecology (Ob/Gyn) clerkship directors regarding timing and methods for teaching and assessment of pelvic and breast examination skills in United States medical school curricula, and to assess clerkship director satisfaction with current educational strategies at their institutions. METHODS: Ob/Gyn clerkship directors from all 135 Liaison Committee on Medical Education accredited allopathic United States medical schools were invited to complete an anonymous 15-item web-based questionnaire. RESULTS: The response rate was 70%. Pelvic and breast examinations are most commonly taught during the second and third years of medical school. Pelvic examinations are primarily taught during the Ob/Gyn and Family Medicine (FM) clerkships, while breast examinations are taught during the Ob/Gyn, Surgery and FM clerkships. GTAs teach pelvic and breast examinations at 72 and 65% of schools, respectively. Over 60% of schools use some type of simulation to teach examination skills. Direct observation by Ob/Gyn faculty is used to evaluate pelvic exam skills at 87% of schools and breast exam skills at 80% of schools. Only 40% of Ob/Gyn clerkship directors rated pelvic examination training as excellent, while 18% rated breast examination training as excellent. CONCLUSIONS: Pelvic and breast examinations are most commonly taught during the Ob/Gyn clerkship using GTAs, simulation trainers and clinical patients, and are assessed by direct faculty observation during the Ob/Gyn clerkship. While the majority of Ob/Gyn clerkship directors were not highly satisfied with either pelvic or breast examination training programs, they were less likely to describe their breast examination training programs as excellent as compared to pelvic examination training-overall suggesting an opportunity for improvement. The survey results will be useful in identifying future challenges in teaching such skills in a cost-effective manner.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.013
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.017
GPT teacher head0.330
Teacher spread0.313 · 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 source (direct Gemma or distilled Codex), 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

Citations26
Published2016
Admission routes1
Has abstractyes

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