Gender Disparities in First Authorship in Publications Related to Attention Deficit Hyperkinetic Disorder (ADHD) and Artificial Intelligence (AI)
Bibliographic record
Abstract
The medical profession has experienced a significant increase in the number of women practitioners in recent decades, leading to a reduction in the gender gap. According to the United States Medical Association, approximately 25% of physicians in the United States are now women. Although this progress is evident in the clinical setting, women's representation in academic medicine remains disproportionately low. The underrepresentation of women in academia has various consequences, including limited access to academic resources and hindered career growth. Previous studies have attempted to analyze these disparities, but results have been inconsistent, and the issue's complexity has not been fully understood. This study aims to examine the disparity in the gender of first authors in academic publications related to " Artificial intelligence (AI) and Attention Deficit Hyperkinetic Disorder (ADHD)" between 2010 and 2023. Analysis was conducted on June 21st, 2023, using the database PubMed. The search term "AI" AND "ADHD" was used to derive all articles over a period of 13 years, from January 1st, 2010, to December 31st, 2022, excluding the year 2023 due to limited available publications. The relevant articles were downloaded in Microsoft Excel sheets. The gender of the first authors was determined using the NamSor app V.2, an application programming interface (API) with a large dataset of names and countries of origin. A total of 204 articles were considered for this study. There were 78 female first authors and 126 male first authors. The highest number of publications with a male first author occurred in 2022, with 32 publications. The Netherlands, Singapore, Turkey, and China have the highest gender ratios, indicating a more favourable representation of both genders. The p-value of 0.2664 suggests that there is no significant association between gender and country. The findings revealed a gender disparity, with a higher number of male first authors. By addressing and rectifying these disparities, we can enhance the overall quality, diversity, and inclusivity of research in the field of ADHD and Artificial Intelligence.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".