Pelvic and breast examination skills curricula in United States medical schools: a survey of obstetrics and gynecology clerkship directors
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".