Women in Academic Pathology: Pathways to Department Chair
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 it