Influences for Gender Disparity in Academic Psychiatry in the United States
Bibliographic record
Abstract
Introduction Academic undertakings, including research, lead to career progression. However, the career paths of female psychiatrists appear to diverge significantly from that of their male counterparts. This article reviews the pervasiveness of the trend of women being less likely to pursue active research in psychiatry. In addition, we examine the correlation between academic rank and research productivity. Methods We searched the American Medical Association's (AMA) Fellowship and Residency Electronic Interactive Database (FREIDA) to identify training programs for psychiatry. A total of 5234 psychiatrists met our inclusion criteria. The gender, academic rank, research work, and h-index of faculty members were compared. The ratio of women reaching senior ranks as compared to men was also calculated. The Scopus database was used to determine the h-index of the individuals included in this study. Data analysis was done with SPSS 22.0 Release 2013 (IBM SPSS Statistics for Windows, IBM, Armonk, NY, USA). Kruskal-Wallis and Mann-Whitney U tests were used where required, with the P-value set at less than 0.05. Results In our study sample, 2181 (42%) of the psychiatrists were women. However, according to the information obtained from the websites of 23 programs, few women reached higher ranks, full professorship, or positions such as the chairperson of a program, and only 9% of women achieved the designation of chairperson of the psychiatry department, with men representing the other 91%. Higher academic rank correlated with higher h-index. A statistically-significant difference between the genders in terms of h-index was found for the assistant professor rank as well. However, this difference was not observed at the level of an associate professor. Conclusions Despite adequate representation of women in the academic workforce in psychiatry, there appears to be a discrepancy in the research productivity of the two genders. This study highlights the need for targeted interventions to address gender disparities in academic psychiatry.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".