What all physicians should know about women’s health: a Delphi study
Bibliographic record
Abstract
Objectives: Over the past few decades, knowledge of women's health regarding sex and gender differences in health has increased but transfer of these new insights into medical education and clinical practice is lagging, resulting in substandard care for women compared with men. This study aimed to reach consensus on what all physicians taking care of women should know about women's health. Methods: A Delphi study was executed involving statements prepared by experts in women's health across 10 medical specialties and a patient advisory board. Participants were recruited from Europe and Northern America through the experts' networks and snowball sampling. Participants voted IN/OUT on each statement based on its perceived relevance and feasibility for general physician knowledge, regardless of specialty. The statements were ranked according to a >80% consensus in the first Delphi round and a 4-point Likert scale in the second Delphi round. Results: In the first round, 44 participants fully completed the survey. 18 statements progressed to the second round, in which four additional statements were included based on participant suggestions. In the final round, 35 responses on the 22 selected statements resulted in consensus on 18 statements of the highest importance, within the following domains: the societal position of women in health, patient perception of disease and treatment, differences in symptomatology, pharmacological considerations and the impact of the female life cycle on health and disease. Conclusion: Consensus was reached on the top priority clinical conditions and public health issues in women's health, resulting in a list of 18 statements on women's health that every physician caring for women should know, regardless of specialty. There was also consensus on the importance of incorporating these insights into medical education. The next step involves implementing women's health education in medical schools, postgraduate education and continuing education for medical specialists.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".