Sex Disparity Among Faculty of Physiology in North American Academia: Differences in Scholarly Productivity and Academic Rank
Bibliographic record
Abstract
Medical academic research done in various specialties shows sex disparity in terms of academic and leadership rank. Research shows that in many medical academic research fields, there are a greater number of men with higher academic and leadership ranks, as well as higher research productivity. This begs the question: What is the case for medical academic research specifically in physiology departments throughout North America? Upon review of the literature, we found that a knowledge gap still exists in North America regarding sex differences among the faculty of physiology. Our rationale for this study is that if a sex disparity among the faculty of physiology in North American academia is found, steps can be taken to lower this disparity. The very first step is identifying that a problem exists. Scopus was used to obtain the h-index, years of active research, and the number of publications and citations of each faculty member. The h-index was used as a metric of academic output and scholarly productivity. Univariate regression was run with the h-index as the outcome of interest and multiple linear regression analysis was used to determine factors associated with a higher h-index. The analysis showed that while the overall number of females holding academic positions in physiology departments throughout North America has increased over the years, a large sex disparity still exists between males and females in the field. This disparity exists not only in academic and leadership rank but also in research productivity, a key predictor of success in the field. This finding warrants that further work be done to find what is causing this disparity and how it can be addressed.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".