Institutional Support for the Career Advancement of Women Faculty in Science and Academic Medicine: Successes, Challenges, and Future Directions
Bibliographic record
Abstract
Institutional support is crucial for the successful career advancement of all faculty but in particular those who are women. Evolving from the past, in which gender disparities were prevalent in many institutions, recent decades have witnessed significant progress in supporting the career advancement of women faculty in science and academic medicine. However, continued advancement is necessary as previously unrecognized needs and new opportunities for improvement emerge. To identify the needs, opportunities, and potential challenges encountered by women faculty, the Women's Leadership Committee of the Arteriosclerosis, Thrombosis, and Vascular Biology Council developed an initiative termed GROWTH (Generating Resources and Opportunities for Women in Technology and Health). The committee designed a survey questionnaire and interviewed 19 leaders with roles and responsibilities in faculty development from a total of 12 institutions across various regions of the United States. The results were compiled, analyzed, and discussed. Based on our interviews and analyses, we present the current status of these representative institutions in supporting faculty development, highlighting efforts specific to women faculty. Through the experiences, insights, and vision of these leaders, we identified success stories, challenges, and future priorities. Our article provides a primer and a snapshot of institutional efforts to support the advancement of women faculty. Importantly, this article can serve as a reference and resource for academic entities seeking ideas to gauge their commitment level to women faculty and to implement new initiatives. Additionally, this article can provide guidance and strategies for women faculty as they seek support and resources from their current or prospective institutions when pursuing new career opportunities.
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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 | 0.023 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".