Gender differences in women’s health and maternity care training: A scoping review
Bibliographic record
Abstract
This article was migrated. The article was marked as recommended. Women's health and maternity care is a core component of the practice of comprehensive family medicine in Canada. The College of Family Physicians of Canada (CFPC) requires that all learners achieve clinical competencies in these skills prior to starting independent practice. Through our integrated women's health program at the University of Alberta, Canada, we aim to train all learners in these required skills. However, despite our intentions, general program evaluation reveals differences in clinical experiences based on a learner's gender. The objective of the present scoping review of published literature was to examine the prevalence of gender differences in women's health and maternity care training, and to identify educational opportunities to ensure that the clinical curriculum provides equitable exposures to learners of all genders. Several publications in our review revealed that male learners had fewer clinical encounters than female learners, while others demonstrated that male learners felt a bias against them during their women's health and maternity care rotations. It was suggested that these differences may result from patient refusal or discrimination against the learner by training staff, and may lead the learner to perform less well on clinical assessments and have decreased comfort and interest in this area of practice. Suggested approaches to minimize these differences included encouraging patients to consent to care by a learner, supporting learners while on these clinical experiences, and providing faculty development to clinical educators. Further research into strategies to narrow the gap in gender differences in clinical experience in women's health and maternity care is warranted.
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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 | 0.011 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.013 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".