Women’s role in neurosurgical research: is the gender gap improving?
Bibliographic record
Abstract
OBJECTIVE: The percentage of women publishing high-impact neurosurgical research might be perceived as a representation of our specialty and may influence the perpetuation of the existing gender gap. This study investigated whether the trend in women taking lead roles in neurosurgical research has mirrored the increase in female neurosurgeons during the past decade and whether our most prestigious publications portray enough female role models to stimulate gender diversity among the new generation of neurosurgeons. METHODS: Two of the most prominent neurosurgical journals-Journal of Neurosurgery and Neurosurgery-were selected for this study, and every original article that was published in 2009 and 2019 in each of those journals was investigated according to the gender of the first and senior authors, their academic titles, their affiliations, and their institutions' region. RESULTS: A total of 1328 articles were analyzed. The percentage of female authors was significantly higher in Europe and Russia compared with the US and Canada (first authors: 60/302 [19.9%] vs 109/829 [13.1%], p = 0.005; and senior authors: 32/302 [10.6%] vs 57/829 [6.9%], p = 0.040). Significantly increased female authorship was observed from 2009 to 2019, and overall numbers of both first and senior female authors almost doubled. However, when analyzing by regions, female authorship increased significantly only in the US and Canada. Female authors of neurosurgical research articles were significantly less likely to hold an MD degree compared with men. Female neurosurgeons serving as senior authors were represented in only 3.6% (48/1328) of articles. Women serving as senior authors were more likely to have a female colleague listed as the first author of their research (29/97 [29.9%] vs 155/1231 [12.6%]; χ2 = 22.561, p = 0.001). CONCLUSIONS: Although this work showed an encouraging increase in the number of women publishing high-impact neurosurgical research, the stagnant trend in Europe may suggest that a glass ceiling has been reached and further advances in equity would require more aggressive measures. The differences in the researchers' profiles (academic title and affiliation) suggest an even wider gender gap. Cultural unconscious bias may explain why female senior authors have more than double the number of women serving as their junior authors compared with men. While changes in the workforce happen, strategies such as publishing specific issues on women, encouraging female editorials, and working toward more gender-balanced editorial boards may help our journals to portray a more equitable specialty that would not discourage bright female candidates.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".