To the Point: Integrating Patient Safety Education Into the Obstetrics and Gynecology Undergraduate Curriculum
Bibliographic record
Abstract
This article is part of the To the Point Series prepared by the Association of Professors of Gynecology and Obstetrics Undergraduate Medical Education Committee. Principles and education in patient safety have been well integrated into academic obstetrics and gynecology practices, although progress in safety profiles has been frustratingly slow. Medical students have not been included in the majority of these ambulatory practice or hospital-based initiatives. Both the Association of American Medical Colleges and Accreditation Council for Graduate Medical Education have recommended incorporating students into safe practices. The Accreditation Council for Graduate Medical Education milestone 1 for entering interns includes competencies in patient safety. We present data and initiatives in patient safety, which have been successfully used in undergraduate and graduate medical education. In addition, this article demonstrates how using student feedback to assess sentinel events can enhance safe practice and quality improvement programs. Resources and implementation tools will be discussed to provide a template for incorporation into educational programs and institutions. Medical student involvement in the culture of safety is necessary for the delivery of both high-quality education and high-quality patient care. It is essential to incorporate students into the ongoing development of patient safety curricula in obstetrics and gynecology.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".