Gender Differences in Academic Rank, Leadership, and Awards Among NIH Grant Recipients in Diagnostic Radiology
Bibliographic record
Abstract
Objective: Females have been traditionally underrepresented in academia across multiple medical specialties, including radiology. The present study investigated primary investigators (PIs) who received National Institutes of Health (NIH) radiology funding between 2016 and 2019 to establish if there was a correlation between NIH grants, gender, academic rank, first and second tier leadership positions, geographic location, and professional awards. Materials and Methods: Funding information was obtained from the NIH Research Portfolio Online Reporting Tools Expenditure and Results (RePORTER) website for 2016–2019. Information for each PI was obtained from academic institutional websites, LinkedIn, and Doximity. Mann–Whitney U tests and chi-square analyses were performed to compare and determine associations between gender and the stated variables of interest. Results: Of the 805 radiology PIs included in this study, 78% were male. There was a significant association of gender with the attainment of the highest academic rank ( p = 0.026), with females occupied more of the assistant professor ranks (M:F = 1:1.5) and less of the professor ranks (F:M = 1:1.2). Between genders, there was no significant difference in first and second tier leadership positions ( p = 0.497, p = 0.116), and postgraduate honors and awards ( p = 0.149). The greatest proportion of grants was awarded in the setting of sole male PIs (55%) and the least proportion of grants were awarded when the contact PI and other project leader were female (1%). Conclusion: Despite having similar academic credentials, including number of leadership positions and postgraduate honors and awards, female radiology PIs who have received NIH grants continue to be underrepresented in higher academic ranks.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.006 | 0.001 |
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".