Gender Disparity in Academic Rank and Productivity Among Public Health Physician Faculty in North America
Bibliographic record
Abstract
Background The issue of gender disparity is particularly important in the domain of public health where the tone of its leadership is pivotal in bringing about impactful change to research, policies, and the wellbeing of our various populations. Our aim is to explore the gender disparity of author metrics and academic rankings of public health physician faculty through a cross-sectional study. Methods Data collection for this retrospective cross-sectional study took place during June and July of 2017. Public health and preventive medicine residency training programs in the United States and Canada were to compiled and all faculty members that met the inclusion criteria were recorded (n = 973). Variables of interest include gender, h-index, years of active research, and academic appointments. SCOPUS database (Elsevier, Amsterdam, the Netherlands) was used to generate author metrics, and all statistical tests were performed using Statistical Package for the Social Sciences (SPSS) software version 20 (IBM Corp., Armonk, NY). Results Overall, 31.14% (n = 303) of faculty members we studied were from Canada, and 68.86% (n = 670) were from the United States. In both countries, males made up the majority of all faculty members. Female faculty comprised most of the early career positions, and their proportions tapered off with higher academic rank, whereas male faculty trended in the opposite direction. Males generally were higher in all academic measures across all appointments. Conclusions Gender disparity exists within the North American public health and preventive medicine discipline. There are underlying factors preventing women from moving beyond early career positions or engaging in academic research.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".