A trend, analysis, and solution on women's representation in diagnostic radiology in North America: a narrative review
Bibliographic record
Abstract
Despite the demonstrated benefits of gender diversity in medicine, women in Radiology in North America are still underrepresented. We reviewed the literature to highlight the current status of women in Radiology in North America, identify the underlying causes of the gender gap, and provide potential strategies to close this gap. We conducted a narrative literature review using the terms ("Gender Disparity" OR "Gender Inequality") AND ("Radiology Department" OR "Radiology Residency"), searching data from April 2000 to April 2022 in Ovid Medline, Embase, PubMed, and Scopus. Our results indicate that Radiology in North America lacks gender diversity in its subspecialties, academic leadership, and research productivity, which the COVID-19 pandemic has further exacerbated. Challenges stemming from a dearth of women role models, limited preclinical contact, and a high rate of burnout contribute to the current gender inequality. Several complementary and supplementary steps can enhance gender diversity in Radiology. These include increasing education and exposure to Radiology at earlier stages and optimizing mentorship opportunities to attract a more diverse pool of talent to the discipline. In addition, supporting resident parents and enhancing the residency program's culture can decrease the rate of burnout and encourage women to pursue careers and leadership positions in Radiology.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".