Impact of a Student-Run Free Clinic’s Women’s Health Program on Perceived Readiness for Clinical Rotations
Bibliographic record
Abstract
INTRODUCTION: Women's health is only briefly explored in the preclerkship medical curriculum. Volunteering in student-run free clinics (SRFCs) increases clinical confidence; such service learning could bridge the gap between limited curricular offerings and student desire for exposure to women's health topics. This study aimed to identify weaknesses in the women's health preclerkship curriculum, build an educational intervention, and explore SRFCs as a teaching tool. METHODS: We performed chart review of SRFC female patients to evaluate care. We held student focus groups to elicit feedback about the established curriculum. Based on this information, we devised a workshop to review practical skills. Participants attended the workshop, volunteered at SRFC, and completed surveys preintervention and at 3 months postintervention. A control group completed baseline and follow-up surveys. RESULTS: We invited all 151 second-year students to participate; six attended the workshop and 21 served as control. There were no baseline differences between groups regarding age, prior experience with women's health, confidence in relevant skills, and subjective readiness for clinical rotations; the control group had more men. After the workshop, intervention participants reported increased confidence in women's health-related skills and in readiness for the OB/GYN rotation. Gains persisted at 3 months. Three of six students in the workshop group volunteered at SRFC; three of 12 in the control group volunteered. CONCLUSIONS: The addition of an interactive workshop to the existing preclinical curriculum on women's health has lasting impact on subjective readiness for clinical clerkships. SRFC may be a useful addition to classroom learning. This initiative is student-led and reproducible, and could serve as an adjunct to established preclerkship curriculum.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".