Vulvovaginal Disease Education in Canadian and American Gynecology Residency Programs: A Survey of Program Directors
Bibliographic record
Abstract
OBJECTIVE: The aims of the study were to assess and describe the current vulvovaginal curriculum in gynecology residency training programs in Canada and the United States and to compare this with national training objectives. MATERIALS AND METHODS: A 22-question electronic survey was sent to 252 gynecology program directors in Canada and the United States between September 2015 and July 2016 using the platform SurveyMonkey.com. Survey responses were entered into SPSS Version 23, and analysis was performed using descriptive statistics. RESULTS: Overall, 58 (23%) of 252 programs directors responded. Nearly all of the sites provided formal teaching on pain disorders (54/58, 93%), vulvar dermatoses (54/58, 93%), and vulvovaginal infections (57/58, 98%). Exposure to vulvovaginal clinics varied widely. On average, program directors estimated that residents spend a median of 10 hours (0-200) in vulvar pain clinics, 9 hours (0-200) in dermatology clinics, and 50 hours (0-480) in colposcopy clinics during residency training. Most program directors (53/57, 93%) believed that all general gynecologists should be able to manage vulvar disorders in practice. Reported obstacles to treating vulvar disorders included lack of training (41/58, 71%) and lack of interest (35/58, 60%). CONCLUSIONS: While most residency programs provided formal education on vulvovaginal diseases, clinical exposure is extremely variable between sites. When it is not possible to increase clinical exposure to vulvovaginal disorders, traditional training methods (lectures, textbooks) should be supplemented with online modules and other means of learning to improve resident knowledge of vulvovaginal diseases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".