Inaugural findings on diversity, equity, and inclusion in Canadian Dental Academia
Bibliographic record
Abstract
PURPOSES/OBJECTIVES: To present the latest landscape on diversity, equity, and inclusion (DEI) focused on people within Canadian dental and dental hygiene academic environments through results obtained from the 2022 American Dental Education Association (ADEA) climate study. METHODOLOGY: This cross-sectional survey investigated the participant`s views on the workplace climate. Students, staff, and faculty members from dental schools and dental hygiene programs participated. The survey was distributed by ADEA in Canada between January and March 2022. The survey was composed of 120 statements divided into domains/topics. The statement included Yes/No, Yes/No/I Do Not Know, open-ended, multiple choice using a 5-point Likert scale, and written words. The higher the mean value on the Likert scale (close to 5), the higher the agreement with the statement (strongly agree). Some statements were reported as percentages. Open-ended questions were reported descriptively. Written words were used to construct word clouds. RESULTS: Eight (83.3%) out of 10 Dental Schools and 12 (32.4%) out of 37 Dental Hygiene Programs participated. Total number of participants was 907 individuals. The workplace climate in Canadian Dental Schools and Dental Hygiene Programs was rated 3.46 (± 1.16) on a Likert Scale from 1 to 5, and it was described upon 1307 affirmative and 607 negative words expressing mostly feelings. Likert Scale means remained between 3.49 and 4.57. CONCLUSIONS: Participants were neutral with a tendency to be satisfied with the overall environment of their workplace. Participants were neutral in relation to well-being, sense of belonging, and inclusive culture. They agreed that their workplace had a welcoming atmosphere, equitable practices, and policies, and was culturally adequate. Several participants experienced and/or witnessed microaggressions, discrimination, and harassment, and most of them have not reported the incident. Strategies to make the school/program more inclusive included orientation activities and appreciation celebrations.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".