Gender‐based dynamics in the 2022 ADEA Climate Study: Perspectives of faculty, staff, and administrators
Bibliographic record
Abstract
OBJECTIVES: This study aimed to assess gender-based perceptions among faculty, staff, and administrators within the 2022 American Dental Education Association (ADEA) Climate Study, focusing on the broad areas of cultural climate, well-being, sense of belonging, inclusivity, authentic identity, and institutional practice and policies. METHODS: The ADEA Climate Study Survey was conducted from January to March 2022, targeting dental and allied dental education programs in the United States and Canada. Data were categorized into men, women, and gender-diverse groups. Descriptive statistics and various comparative analyses (Tukey test for pairwise mean comparison, Kruskal-Wallis chi-square, Pearson's chi-squared, and Fisher's exact tests) were employed to evaluate perceptions across these groups. RESULTS: The current analyses included 5396 participants, with women comprising the majority (68.8%), followed by men (30.1%) and gender-diverse individuals (1.1%). Respondents were faculty (56.1%), staff (32.3%), administrators (4.9%), and administrators with faculty appointments (6.7%). Significant gender disparities were observed in satisfaction with health/well-being support, sense of belonging, and respect within institutions. Women and gender-diverse individuals reported lower satisfaction than men. They also perceived their opinions as less valued and faced more challenges in building trusting relationships. Institutional policies on harassment, bullying, and discrimination received less favorable ratings from women and gender-diverse individuals, who also reported higher rates of harassment and discrimination. CONCLUSIONS: This study highlights gender disparities in dental academia, emphasizing diverse perceptions and experiences among faculty and staff based on gender identity. Understanding these dynamics can inform dental schools and allied educational programs as they proactively implement diversity initiatives, enhance career development opportunities, and enact robust policies fostering diversity, equity, inclusion, and belonging.
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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 | 0.007 | 0.007 |
| 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 | 0.000 | 0.003 |
| 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".