Exploring the climate for lesbian, gay, bisexual, and queer faculty, administrators, and staff in oral health education: Insights from the American Dental Education Association 2022 climate study
Bibliographic record
Abstract
PURPOSE/OBJECTIVES: The study aims to examine the perceptions and experiences of lesbian, gay, bisexual, and queer (LGBQ+) faculty, administrators, and staff regarding campus climate in the United States and Canadian dental schools and allied dental education programs. This research focuses on sexual orientation, excluding gender identity, to provide a clearer understanding of LGBQ+ experiences in academic dentistry. METHODS: The American Dental Education Association climate study survey was conducted from January to March 2022 across dental schools and allied dental education programs in the United States and Canada. An independent ethical review board approved the survey. Data were collected from faculty, administrators, and staff who identified as LGBQ+ or heterosexual. The survey responses were analyzed using chi-square tests and proportional odds models to compare the experiences of LGBQ+ respondents with their heterosexual counterparts. RESULTS: The study included 162 administrators, 1274 faculty, and 326 staff from allied dental schools, and 641 administrators, 2752 faculty, and 2270 staff from dental schools. Key findings indicate that LGBQ+ respondents were less likely to feel a sense of belonging, perceive a humanistic environment, or be satisfied with diversity programs. They were also more likely to report experiences of discrimination and harassment. The study identified significant disparities in perceptions of leadership commitment to diversity and inclusivity between LGBQ+ and heterosexual respondents. CONCLUSIONS: The findings highlight the need for dental education institutions to enhance efforts in fostering a supportive and inclusive environment for LGBQ+ faculty, administrators, and staff. Recommendations include improving community-building activities, promoting a humanistic environment, revising hiring practices, implementing zero-tolerance policies for discrimination and harassment, and increasing awareness and engagement in Diversity, Equity, and Inclusion initiatives. Addressing these issues is crucial for improving the overall climate and ensuring the well-being and productivity of LGBQ+ individuals 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".