Development of the Big Ten Academic Alliance Collaborative for Women in Medicine and Biomedical Science: “We Built the Airplane While Flying It”
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,049 | 0,032 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,011 | 0,003 |
| Communication savante | 0,006 | 0,005 |
| Science ouverte | 0,004 | 0,021 |
| Intégrité de la recherche | 0,003 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,023 | 0,007 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».