Have Women Broken the Glass Ceiling in North American Dental Leadership?
Bibliographic record
Abstract
In the last few decades, the number of women graduating from North American (NA) dental schools has increased significantly. Thus, we aimed to determine women's representation in leadership positions in NA dental and specialty associations/organizations, dental education, and dental journals, as well as the proportion of men/women researcher members of the American Association for Dental Research (AADR). We contacted NA dental associations to provide us with the total number and the men/women distribution of their members. Men/women distributions in leadership positions were accessible from the internet, as were data on the sex of deans of NA dental schools. Data on the editors in chief of NA dental journals were gathered from their websites, and the AADR provided the number and sex of its researcher members. Collected data underwent descriptive statistics and binomial tests (α = 0.05). Our findings suggest that women are underrepresented in leadership positions within the major NA dental professional associations. While the median ratio of women leaders to women members in professional associations is 0.91 in Canada, it is only 0.67 in the United States. The same underrepresentation of women is evident in the leadership of the Canadian Dental Association and the American Dental Association. We found that women are underrepresented as deans and editors in chief for NA oral health journals. Only 16 of 77 NA dental school deans are women, while 3 of 38 dental journals have women editors in chief. The probability of finding these ratios by chance is low. However, the number of women dental researcher AADR members underwent an overall increase in the past decade, while the number of men declined. These results suggest that, despite the increase in women dentists, it will take time and effort to ensure that they move through the pipeline to senior leadership positions in the same manner as their male colleagues.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".