Influences for Gender Disparity in Academic Neuroradiology
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: There has been extensive interest in promoting gender equality within radiology, a predominately male field. In this study, our aim was to quantify gender representation in neuroradiology faculty rankings and determine any related factors that may contribute to any such disparity. MATERIALS AND METHODS: We evaluated the academic and administrative faculty members of neuroradiology divisions for all on-line listed programs in the US and Canada. After excluding programs that did not fulfill our selection criteria, we generated a short list of 85 US and 8 Canadian programs. We found 465 faculty members who met the inclusion criteria for our study. We used Elsevier's SCOPUS for gathering the data pertaining to the publications, H-index, citations, and tenure of the productivity of each faculty member. RESULTS: = .009). The median H-index was higher among male faculty members (17.5) versus female faculty members (9). Female faculty members have odds of 0.84 compared with male faculty members of having a higher H-index, adjusting for publications, citations, academic ranks, leadership ranks, and interaction between gender and publications and gender and citations (9). CONCLUSIONS: Neuroradiology faculty members follow the same male predominance seen in many other specialties of medicine. In this study, issues such as mentoring, role models, opportunities to engage in leadership/research activities, funding opportunities, and mindfulness regarding research productivity are explored.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".