Gender trends in dentistry: Dental faculty and academic leadership
Bibliographic record
Abstract
OBJECTIVES: Gender equality in the healthcare workforce has been a topic of discourse for many decades. In dental academia, women's representation of enrolled students and faculty has risen consistently since the 1980s. However, women in faculty leadership positions may still be lagging when compared to men. The purpose of this study was to evaluate the number of women who occupy the upper echelons of academic rank and title by analyzing cross-referenced data from the American Dental Association and the American Dental Education Association on women dental school graduates in relation to academic appointments. METHODS: Gender distribution in rank, title, and appointments in the decade from 2011 to 2019, as well as percentage of women graduates over the same period, were collected for descriptive statistics. Multiple linear regression analysis, Cochran Armitage, and chi-square tests were conducted to examine trends over the years and to determine significant differences in overall percentages (p < 0.05). RESULTS: The total women faculty percentages ranged from 36% to 40%. While the lower-level rank of instructor retained a higher representation of women (56%-65%), the higher rank of professor had disproportionately lower women percentages (18%-26%). Graduates, full-time faculty, lower-level academic ranks, and higher-level academic ranks for women followed similar upward trends that were statistically significant (p < 0.05). When comparing the different groups against each other, the annual increase in women DMD/DDS graduate percentage was higher than women full-time faculty (0.28%), instructor rank (0.92%), professor rank (0.50%), and department chair appointments (0.49%). CONCLUSIONS: Our data show that women are still underrepresented at higher academic ranks. However, the upward trends for professors, assistant deans, and program chairs suggest that in recent years, more women faculty may have been encouraged, mentored, or offered higher administrative positions in academic institutions.
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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 | 0.002 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".