Gender-Inclusive Fellowship Naming and Equity, Diversity, and Inclusion in Radiology: An Analysis of Radiology Department Websites in Canada and the United States
Bibliographic record
Abstract
Purpose: Transgender and gender non-binary (TGNB) individuals face numerous inequalities in healthcare and there is substantial work to be done in fostering TGNB culturally competent care in radiology. A radiology department’s online presence and use of gender-inclusive language are essential in promoting an environment of equity, diversity, and inclusion (EDI). The naming of radiology fellowships and continuing medical education (CME) courses with terminology such as “Women’s Imaging” indicates a lack of inclusivity to TGNB patients and providers, which could result in suboptimal patient care. Methods: We conducted a cross-sectional analysis of all institutions in Canada and the United States (US) offering training in Breast Imaging, Women’s Imaging, or Breast and Body Imaging. Data was collected from each institution’s radiology department website pertaining to fellowship names, EDI involvement, and CME courses. Results: 8 Canadian and 71 US radiology fellowships were identified. 75% of Canadian and 90% of US fellowships had gender-inclusive names. One (12.5%) Canadian and 29 (41%) US institutions had EDI Committees mentioned on their websites. Among institutions publicly displaying CME courses about breast/body or women’s imaging, gender-inclusive names were used in only 1 (25%) of the Canadian CME courses, compared to 81% of the US institutions. Conclusions: Most institutions in Canada and the US have gender-inclusive names for their radiology fellowships pertaining to breast and body imaging. However, there is much opportunity to and arguably the responsibility for institutions in both countries to increase the impact and visibility of their EDI efforts through creation of department-specific committees and CME courses.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".