Education for Women Physicians and Their Continuing Professional Development: Lessons from Canada
Bibliographic record
Abstract
日本における女性医師の比率は，2016年では21. ％である。一方，OECD加盟国では概ね医師の2人に1人が女性という状況である（2015年）。海外における女性医師の養成並びに生涯学習の状況を知るために，カナダの医学教育，医師の専門分野における男女統計，女性医師団体による継続専門教育（Continuing Professional Development）の実践を考察する。 カナダにおける女性医師比率は41.2％であるが，医師養成課程の在籍者数においては，1995年に50％を超え，2015年では55.1％である。医学教育の場では女性が多数派となり20年が経っているが，ワーク・ライフ・バランスやリーダーシップなどの面で課題は残されている。そのため，女性医師団体はネットワーキング活動と共に，カナダ専門医協会が定める継続専門教育の機会を提供し，女性のキャリア形成支援を行っている。
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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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".