Women in medicine: a four-nation comparison.
Bibliographic record
Abstract
OBJECTIVES: to determine the impact of increasing numbers of women in medicine on the physician work force in Australia, Canada, England, and the United States. METHODS: We collected data on physician work force issues from professional organizations and government agencies in each of the 4 nations. RESULTS: Women now make up nearly half of all medical students in all 4 countries and 20% to 30% of all practicing physicians. Most are concentrated in primary care specialties and obstetrics/gynecology and are underrepresented in surgical training programs. Women physicians practice largely in urban settings and work 7 to 11 fewer hours per week than men do, for lower pay. Twenty percent to 50% of women primary care physicians are in part-time practice. CONCLUSIONS: Work force planners should anticipate larger decreases in physician full-time equivalencies than previously expected because of the increased number of women in practice and their tendency to work fewer hours and to be in part-time practice, especially in primary care. Responses to these changes vary among the 4 countries. Canada has developed a detailed database of work/family issues; England has pioneered flexible training schemes and reentry training programs; and Australia has joined consumers, physicians, and educators in improving training opportunities and the work climate for women. Improved access to surgical and subspecialty fields, training and practice settings that provide balance for work/family issues, and improved recruitment and retention of women physicians in rural areas will increase the contributions of women physicians.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".