Beyond diversity, equity, and inclusion: American Dental Education Association's role in inclusivity, humanism, and leadership
Bibliographic record
Abstract
In 2022, the American Dental Education Association (ADEA) launched the first-ever dental education-wide climate assessment survey to establish baseline data on diversity, equity, and inclusion (DEI). This article aims to highlight the historical role of ADEA in supporting oral health education while building the inclusive capacity of leaders and advancing its organizational mission and vision in promoting DEI. The survey is a significant step in assisting academic dentistry in promoting a more humanistic environment while measuring the perception of students, faculty, staff, and leadership regarding DEI. ADEA has significantly contributed to advancing dental education through data collection and the development of initiatives that enhance DEI across dental schools and allied education programs in the United States and Canada. The ADEA's efforts underscore its commitment to enhancing diversity, equity, and inclusion, aligning with its broader mission to improve oral health 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 | 0.034 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.013 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.001 | 0.019 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".