Perceptions of the dental education‐wide climate: Analysis of the 2022 ADEA climate study focus groups
Bibliographic record
Abstract
OBJECTIVE: Climate assessments in higher and postsecondary education institutions are essential in landscaping inclusiveness and belonging for students, faculty, and staff. Although some climate assessments in dental education have been conducted either as part of their own campus or parent university climate assessment, a dental education-wide climate study has not been conducted across dental schools and allied dental education programs in the United States and Canada. METHODS: As integral part of the 2022 ADEA Climate Survey in Dental Education, focus groups were conducted from March to April 2022 to ascertain the perceptions of students, faculty, and staff in dental education. A phenomenological study on 85 focus group participants comprised of students, faculty, and staff at U.S. and Canadian dental schools and allied dental education programs was conducted. Thematic analyses were structured on four overarching categories: (1) belonging, (2). bias, (3) challenges and barriers, and (4) future recommendations. RESULTS: Several themes emerged across all groups. The lack of inclusion and belonging on campuses, deficit of faculty of color, microaggressions and differential treatment of students of color, and the need to enhance recruitment of diverse students and faculty of color were among themes identified. Exclusive to U.S. allied dental education programs, exposure to unique community and clinical opportunities for students was identified as an important theme to enhance diversity, equity, inclusion, and belonging (DEIB). CONCLUSION: The first-ever dental education-wide climate study exposes the need to undertake this initiative in academic dentistry. The ongoing challenges unveiled in this study offer an opportunity to identify solutions that are meaningful, inclusive, impactful, and that foster humanistic learning environments for students, faculty, and staff in dental education.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".