Increasing Certified Nurse‐Midwives’ Confidence in Managing the Obstetric Care of Women with Female Genital Mutilation/Cutting
Bibliographic record
Abstract
INTRODUCTION: In response to an increase in the number of women who immigrate to the United States from countries that practice female genital mutilation/cutting (FGM/C; infibulation), US clinicians can expand their knowledge and increase confidence in caring for women who have experienced infibulation. This article describes a comprehensive education program on FGM/C and the results of a pilot study that examined its effect on midwives' confidence in caring for women with infibulation. METHODS: An education program was developed that included didactic information, case studies, a cultural roundtable, and a hands-on skills laboratory of deinfibulation and repair. Eleven certified nurse-midwives (CNMs) participated in this pilot study. Participants completed a measure-of-confidence survey tool before and after the education intervention. RESULTS: Participants reported increased confidence in their ability to provide culturally competent care to immigrant women with infibulation when comparisons of preeducation and posteducation survey confidence logs were completed. DISCUSSION: Following the education program and the knowledge gained from it, these midwives were more confident about their ability to perform anterior episiotomy and to deliver necessary care to women with FGM/C in a culturally competent context. This education program should be expanded as more women who have experienced infibulation immigrate to the United States.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".