Promoting women's careers in life science and medicine: A position paper from the “International Women in Intensive Medicine” network
Bibliographic record
Abstract
BACKGROUND: Women remain underrepresented and undervalued in leadership roles within scientific and healthcare disciplines, facing persistent gender discrimination and various professional barriers. METHODS: This paper presents the main findings and recommendations from the 2023 International Women in Intensive and Critical Care Network (iWIN) Roundtable, which convened a diverse group of experts to discuss equity and inclusiveness for women in their careers. RESULTS: The discussion highlighted three critical themes: social barriers (such as maternal identity and cultural pressures), the need for resilience-building through mentorship/sponsorship and support networks; and practical challenges, including childcare and limited career guidance. To address these issues, the panel proposed developing and empowering women's networks, emphasizing their role in promoting gender equality, fostering diversity, and supporting professional development. A key recommendation is the creation of a digital platform to increase the visibility of women scientists and connect them with opportunities for engagement and leadership. Additionally, the paper underscores the importance of institutional support for flexible work arrangements, mentorship programs, and leadership development initiatives. CONCLUSIONS: The proposed strategies aim to not only advance the careers of women in science and healthcare but also to challenge and reshape the stereotypes surrounding who can be a scientist. By providing practical tools and fostering a culture of inclusiveness, these recommendations have the potential to significantly impact the representation of women in these fields and contribute to broader societal change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 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".