Development of the Big Ten Academic Alliance Collaborative for Women in Medicine and Biomedical Science: “We Built the Airplane While Flying It”
Bibliographic record
Abstract
Unlabelled: Women-identifying and women+ gender faculty (hereto described as women+ faculty) face numerous barriers to career advancement in medicine and biomedical sciences. Despite accumulating evidence that career development programming for women+ is critical for professional advancement and well-being, accessibility of these programs is generally limited to small cohorts, only offered to specific disciplines, or otherwise entirely unavailable. Opportunities for additional, targeted career development activities are imperative in developing and retaining women+ faculty. Our goal was the development of a new collaborative of Big Ten Academic Alliance (BTAA) institutions to support gender equity for women+ faculty in medicine and biomedical sciences, with two initial aims: (1) hosting an inaugural conference and establishing a foundation for rotation of conference hosts across BTAA schools, and (2) creating an infrastructure to develop programming, share resources, conduct environmental scans, and promote networking. In 2022, leaders from The Ohio State University College of Medicine Women in Medicine and Science envisioned, developed, and implemented a collaborative named CommUNITYten: The Big Ten Academic Alliance for Women in Medicine and Biomedical Science. Conference program development occurred through an iterative and collaborative process across external and internal task forces alongside industry partners. We developed a fiscal model to guide registration fees, budget tracking, and solicitation of conference funding from academic and industry sponsors. Attendees completed postconference surveys assessing speaker or workshop effectiveness and suggestions for future events. Finally, we developed an environmental scan survey to assess gender equity needs and existing programming across BTAA institutions. In June 2024, The Ohio State University hosted the inaugural CommUNITYten conference in Columbus, Ohio, featuring 5 keynote presentations, 9 breakout sessions, and networking opportunities across one and a half days of curated programming. Nearly 180 people attended, with representation from 9 BTAA institutions, 6 industry companies, staff, and trainees. Postconference surveys showed 50% (n=27) of respondents were likely to attend another in-person conference and suggested future conference topics. The environmental scan survey launched in October 2024. We successfully established the CommUNITYten collaborative and hosted the inaugural conference. Establishing key stakeholders from each BTAA institution, obtaining sponsorship, and detailed conference planning and partnerships were critical in ensuring realization of this collaborative. The conference brought together leaders, faculty, staff, trainees, and industry partners from across the country and met the initial goal of networking, sharing resources, and building community for women+ faculty. These efforts lay a robust foundation for the BTAA CommUNITYten collaborative to foster ongoing collaboration, innovation, and progress in the years to come. Given the importance of steady improvements, this viewpoint may further guide the efforts of other individuals, groups, and leadership supporting women+ as they consider approaches and strategies advocating for gender equity at the national level.
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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 | 0.049 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.023 | 0.007 |
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".