Challenges in Providing Gynecological Procedures in Primary Care: A Survey of Canadian Academic Family Physicians
Bibliographic record
Abstract
Purpose: Globally, there is a lack of access to health care providers who offer gynecological procedures. Understanding the practice patterns of academic family physicians (AFPs) and whether additional training impacts the provision of care is critical. This study surveys the practice patterns of AFPs regarding gynecological procedures offered, identifies barriers, and explores the impact of additional training. Methods: We circulated an anonymous, cross-sectional survey to all 17 family medicine programs across Canada, receiving responses from 71 AFPs. We computed descriptive statistics and bivariate associations. Results: A total of 71 respondents from five universities participated. Most participants (97.2%) performed Papanicolaou (Pap) smears; 67.6% provided intrauterine device (IUD) insertion, and only 54.9% offered endometrial biopsy. Numbers decreased significantly for routine pessary care (29.5%), punch biopsy of the vulva (15.5%), and pessary fitting (5.6%). Eighteen participants (26.9%) had received enhanced skills training with a certificate of added competence (CAC), of which 55.6% were in women’s health. CAC holders in women’s health provided IUD insertions (100% vs. 67.3%; p = 0.049, V = 0.28) and endometrial biopsies (90.0% vs. 53.1%; p = 0.036, V = 0.28) at higher rates than general AFPs. Frequently cited barriers to offering gynecological procedures included lack of knowledge, procedural skills, and insufficient patient volumes to maintain competence. During the COVID-19 pandemic, 44% of respondents reported reducing or ceasing to provide Pap smears. Conclusions: Many AFPs in Canada do not provide essential gynecological procedures. This impacts patient access and the training of the next generation of family physicians and thus requires innovative strategies to address the persistent procedural skills educational gap for trainees.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".