Microaggressions, harassment, and discrimination in dental education: Results from the 2022 American Dental Education Association Climate Study
Bibliographic record
Abstract
OBJECTIVE: Microaggressions, harassment, and discrimination are prevalent in higher and postsecondary education. Although there are some preliminary data in academic dentistry, their extent in this field remains under-researched. This study explores the overall perceptions of participants from the American Dental Education Association (ADEA) Climate Study regarding witnessing or experiencing microaggressions, harassment, and discrimination in dental education. METHODS: Data from the 2022 ADEA Climate Study were analyzed using logistic regression to assess the impact of role, institution type, and geographic regions on experiences of microaggressions, harassment, and discrimination across multiple social identities (race, gender, sexual identity, religious beliefs, etc.). A sentiment analysis was conducted on textual responses to gauge overall climate satisfaction. RESULTS: Administrators with faculty appointments were 1.5 times more likely than other administrators and 1.9 times more likely than staff to report experiences of microaggressions (p < 0.01). Students reported higher rates of microaggressions than faculty (odds ratio [OR] = 1.1, p = 0.02) and staff (OR = 1.2, p < 0.01). Staff were less likely to report any incident of microaggressions, harassment, or discrimination. Administrators with faculty appointments were more likely to witness or experience microaggressions based on other social identities. The Northeast region of the U.S. reported fewer microaggressions compared to other geographical regions, including Canada. Sentiment analysis predicted participants' overall satisfaction with the dental or dental allied program climate. CONCLUSION: Perceptions of microaggressions, harassment, and discrimination vary significantly across different roles and regions in academic dentistry. Addressing these systemic issues is crucial for fostering inclusive and supportive learning environments in academic dentistry.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".