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
Bibliographic record
Abstract
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.
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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 | 0.081 | 0.121 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.147 | 0.033 |
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".