Gender distribution of North American professional radiology society award recipients
Bibliographic record
Abstract
PURPOSE: Women remain underrepresented in radiology and there is a paucity of literature examining the recognition of their professional contributions to the discipline. The purpose of this study was to examine the gender distribution of award winners across all North American radiology societies. METHODS: The gender distribution of 1923 award recipients from 21 North American radiology societies between 1960 and 2021 was examined. Awards were divided into four categories: leadership, teaching, contribution to radiology, and promising new/young societal member. Primary outcome was the total proportion of awards received by gender. All data was compared to the gender distribution of working radiologists in North America. RESULTS: A total of 1923 award recipients were identified between 1960 and 2021. Seventy-nine percent of award recipients were men (n = 1527) and 21 % were women (n = 396). As of 1970, the proportion of women award recipients increased 0.55 % ± 0.07 % each year. The proportion of women receiving radiological awards after 2018 is equal to or surpassing the percentage of women radiologists. Women received 36.4 % of leadership, 33.6 % of promising new member, 30.1 % of teaching, and 14.4 % of lifetime contribution awards. CONCLUSIONS: In the last five years, the proportion of women receiving awards was equal to or greater than the proportion of women radiologists. Women received more leadership awards and fewer lifetime contributor awards compared to men.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.028 | 0.003 |
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".