Institutional Climate Matters: American Dental Education Association United States Climate Study results—Dental and Allied Dental programs
Bibliographic record
Abstract
The aim of this study was to assess the overall climate in US dental schools and allied dental programs. The inaugural American Dental Education Association Climate Study was sent to all US and Canadian dental schools and allied dental programs in 2022. The survey was open to all students, faculty, and staff members in the Commission on Dental Accreditation-accredited dental schools and allied dental education programs in the United States and Canada. The survey questionnaire assessed aspects of campus climate, focusing on areas such as overall climate, inclusive culture, welcomeness, and institutional practices and policies. There were 15,759 respondents (10,457 from dental schools and 5302 from allied dental education programs). Allied dental programs scores were significantly higher than dental school scores on all indices with the highest difference in overall climate. Within allied programs, dental assisting scored significantly higher than dental hygiene in all indices except cultural competence. For both allied dental programs and dental schools, overall climate was strongly positively correlated with inclusive culture The majority of respondents reported they have not witnessed or experienced microaggression for allied dental programs (93%) compared to dental schools (83%) (p < 0.01). Dental school participants reported more often than allied programs that they were aware of a diversity, equity, and inclusion (DEI) office or department (39%, 70%, p < 0.01), dedicated DEI officer (30%, 63%, p < 0.01), and funding for DEI programming (20%, 32%, p < 0.01). The results of this study serve as baseline data for future research. The results of this study show that collaboration and sharing of best practices for inclusive climate in dental schools and allied dental education programs is essential.
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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 | 0.009 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".