Examining Gender Differences in Faculty Promotion Among Canadian Dental Schools
Bibliographic record
Abstract
OBJECTIVES: There is limited research on gender differences in academic dentistry and leadership within Canadian dental schools. This novel study aimed to assess gender trends among dental school faculty, deanship positions, and student convocation, as well as explore faculty perspectives on institutional attitudes toward gender equality in Canadian dental schools. METHODS: Data were gathered and analyzed from all 10 Canadian dental schools' public websites for the categories of academic faculty, deans, and student convocation. An anonymous survey was sent to faculty members (n = 518) through SurveyMonkey. Statistical analysis was performed through IBM SPSS 28.0 to determine if any statistically significant differences in gender occurred. RESULTS: An average of 12.3% more women graduated than men in 2019-2024, a trend not yet present within academic faculty. A statistically significant gender gap exists when comparing assistant, associate, and full professor positions (p = 0.019). In addition, the difference between men and women among individuals holding interim and permanent dean roles is statistically significant (p = < 0.001). Survey results indicate a positive outlook on gender inclusion within institutions; yet a large proportion of respondents ( > 50%) either agreed or were unsure on whether gender influenced their career progression and their ability to negotiate wages. CONCLUSION: This study significantly contributes to the understanding of current gender trends and beliefs that exist in Canadian dental schools. While strides have been made to close the gender gap, there are instances in which these differences should not be ignored. Results emphasize a need for initiatives that support gender parity within the academic setting, such as mentorship opportunities for women, greater institutional support for parents, and better data repositories that make gender trend data more easily accessible. Further research could investigate gender trends within dental specialties/residencies and possible wage disparities that affect dental schools' faculty.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".