Learning on the Go: Assessing Knowledge Gained From Medical Podcasts Created for Vulvovaginal Disease Education
Bibliographic record
Abstract
OBJECTIVE: The aim of the study was to evaluate the effectiveness of "The Vulva Diaries" podcast as a novel learning tool for vulvovaginal disease education. MATERIALS AND METHODS: Medical students and residents were invited to participate in the study using social media advertisements. Online pretests and posttests, one based on a podcast episode regarding genital herpes and the other on lichen sclerosus, were used to assess changes in knowledge level pre- and post-podcast listening in medical students and residents respectively. A second posttest was sent out 2 weeks after the first to assess knowledge retention. Results were analyzed using paired t tests comparing mean scores before and after podcast. RESULTS: In medical students, the average test score increased by 20% (n = 56, p < .001). Similarly, in residents the average test score increased by 23.1% (n = 22, p < .001). Medical students and residents rated their average preference for using podcasts as compared with other resources at 3.6 and 3.7/5, respectively. Furthermore, in both groups, there was no significant difference between average scores for posttest 1 versus posttest 2 written 2 weeks later suggested excellent knowledge retention. CONCLUSIONS: "The Vulva Diaries" podcast increases knowledge on vulvovaginal disease and is an effective learning tool for health care trainees in women's health. This study emphasizes the role of podcasts as a valuable educational resource within gynecology. The success of such initiatives will hopefully bolster the effort to correct the lack of provider knowledge in treating vulvovaginal diseases.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".