Assessing and improving women representation in radiology leadership positions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Gender representation remains a critical issue in professions, especially within medical specialties like radiology, where the representation of women in leadership roles significantly lags. Despite a promising increase in women physicians in Canada, reaching 42.7% by 2019, radiology showcases a stark gender disparity, particularly in leadership positions. This article examines the barriers hindering women's advancement in radiology and proposes actionable solutions to cultivate a more equitable environment. It highlights the underrepresentation of women in radiology leadership across the United States and Canada, with women holding significantly fewer senior academic positions and leadership roles. Key barriers include a lack of women role models, gender-based obstacles in research opportunities, and by design discriminatory practices. Solutions proposed include the establishment of mentorship programs, and inclusive policies at multiple organizational levels such as at the level of trainees, faculty and leadership positions including chair of the department. Additionally, policies and initiatives centred on education and training in unconscious bias, the creation of professional groups for women in radiology, and interventions to address unsafe work environments.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it