Evaluation of an Obstetric Medicine Curriculum for Obstetrics and Gynecology Trainees: A Quality Improvement Study
Bibliographic record
Abstract
OBJECTIVES: Medical disorders in pregnancy are increasing. This highlights the need for obstetrics trainees to develop a strong foundation in managing medical conditions in pregnancy. METHODS: To address this, the internal medicine curriculum at our institution was redesigned for first year obstetrics residents to include an obstetric medicine (OBM) rotation. RESULTS: Before implementation, only 19% of residents felt at least moderately comfortable with OBM topics outlined by the Canadian Consensus for a Curriculum in Obstetric Medicine. This increased to 66% after the introduction of the redesigned curriculum. Using quality improvement methodology, we aimed to increase this to 80% via iterative plan-do-study-act cycles. Post-intervention, 81% of trainees reported feeling at least moderately comfortable in 14 of 17 Canadian Consensus for a Curriculum in Obstetric Medicine topics. CONCLUSIONS: This quality improvement-driven curriculum enhancement was well-received and further highlights the value of early OBM integration in obstetrics training.
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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 | 0.031 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".