Update: A Review of Women's Health Fellowships, Their Role in Interdisciplinary Health Care, and the Need for Accreditation
Bibliographic record
Abstract
While Women's Health (WH) Fellowships have been in existence since 1990, knowledge of their existence seems limited. Specialized training in WH is crucial to educate leaders who can appropriately integrate this multidisciplinary field into academic centers, especially as the demand for providers confident in the areas of contraception, perimenopause/menopause, hormone therapy, osteoporosis, hypoactive sexual desire disorder, medical management of abnormal uterine bleeding, office based care of stress/urge incontinence, and gender-based medicine are increasing popular and highly sought after. WH fellowship programs would benefit from accreditation from the American Board of Medical Subspecialties and from the American College of Graduate Medical Education, as this may allow for greater recruitment, selection, and training of future leaders in WH. This article provides a current review of what WH trained physicians can offer patients, and also highlights the added value that accreditation would offer the field. Ultimately, accrediting WH fellowships will improve women's health medical education by creating specialists that can serve as academic leaders to help infuse gender specific education in primary residencies, as well as serve as consultants and leaders, and promote visibility and prestige of the field.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".