Women in Academic Pathology: Pathways to Department Chair
Bibliographic record
Abstract
The Association of Pathology Chairs, an organization of American and Canadian academic pathology departments, has a record percent of women department chairs in its ranks (31%), although still not representative of the percent of women pathology faculty (43%). These women chairs were surveyed to determine what had impeded and what had facilitated their academic advancement before becoming chairs. The 2 most frequently identified impediments to their career advancement were heavy clinical loads and the lack of time, training, and/or funding to pursue research. Related to the second impediment, only one respondent became chair of a department which was in a top 25 National Institutes of Health-sponsored research medical school. Eighty-nine percent of respondents said that they had experienced gender bias during their careers in pathology, and 31% identified gender bias as an important impediment to advancement. The top facilitator of career advancement before becoming chairs was a supportive family. Strikingly, 98% of respondents have a spouse or partner, 75% have children, and 38% had children younger than 18 when becoming chairs. Additional top facilitators were opportunities to attend national meetings and opportunities to participate in leadership. Previous leadership experiences included directing a clinical service, a residency training program, and/or a medical student education program. These results suggest important ways to increase the success of women in academic pathology and increasing the percent of women department chairs, including supporting a family life and providing time, encouragement and resources for research, attending national meetings, and taking on departmental leadership positions.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 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".