Medicine Goes Female: Protocol for Improving Career Options of Females and Working Conditions for Researching Physicians in Clinical Medical Research by Organizational Transformation and Participatory Design
Notice bibliographique
Résumé
BACKGROUND: All European countries need to increase the number of health professionals in the near future. Most efforts have not brought the expected results so far. The current notion is that this is mainly related to the fact that female physicians will clearly outnumber their male colleagues within a few years in nearly all European countries. Still, women are underrepresented in leadership and research positions throughout Europe. OBJECTIVES: The MedGoFem project addresses multiple perspectives with the participation of multiple stakeholders. The goal is to facilitate the implementation of Gender Equality Plans (GEP) in university hospitals; thereby, transforming the working conditions for women working as researchers and highly qualified physicians simultaneously. Our proposed innovation, a crosscutting topic in all research and clinical activities, must become an essential part of university hospital strategic concepts. METHODS: We capture the current status with gender-sensitive demographic data concerning medical staff and conduct Web-based surveys to identify cultural, country-specific, and interdisciplinary factors conducive to women's academic success. Individual expectations of employees regarding job satisfaction and working conditions will be visualized based on "personal construct theory" through repertory grids. An expert board working out scenarios and a gender topic agenda will identify culture-, nation-, and discipline-specific aspects of gender equality. University hospitals in 7 countries will establish consensus groups, which work on related topics. Hospital management supports the consensus groups, valuates group results, and shares discussion results and suggested measures across groups. Central findings of the consensus groups will be prepared as exemplary case studies for academic teaching on research and work organization, leadership, and management. RESULTS: A discussion group on gender equality in academic medicine will be established on an internationally renowned open-research platform. Project results will be published in peer-reviewed journals with high-impact factors. In addition, workshops on gender dimension in research using the principles of Gendered Innovation will be held. Support and consulting services for hospitals will be introduced in order to develop a European consulting service. CONCLUSIONS: The main impact of the project will be the implementation of innovative GEP tailored to the needs of university hospitals, which will lead to measurable institutional change in gender equality. This will impact the research at university hospitals in general, and will improve career prospects of female researchers in particular. Simultaneously, the gender dimension in medical research as an innovation factor and mandatory topic will be strengthened and integrated in each individual university hospital research activity. Research funding organizations can use the built knowledge to include mandatory topics for funding applications to enforce the use and implementation of GEP in university hospitals.
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 | 0,081 | 0,121 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,003 |
| Méta-épidémiologie (sens large) | 0,003 | 0,004 |
| Bibliométrie | 0,005 | 0,004 |
| Études des sciences et des technologies | 0,006 | 0,003 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,003 | 0,005 |
| Intégrité de la recherche | 0,005 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,147 | 0,033 |
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 ».