Students, staff, faculty, and administrators’ cultural competence and satisfaction: Results of the 2022 American Dental Education Association climate study
Bibliographic record
Abstract
OBJECTIVES: The American Dental Association's Commission on Dental Accreditation requires that graduates function in a diverse environment. The objectives were to assess (a) the self-reported cultural competence and (b) the satisfaction of dental and allied dental students, staff, faculty, administrators, and administrators with faculty appointments (admin/faculty) in the United States and Canada and (c) to explore the relationships between these constructs of interest. METHODS: The analyses were based on data from these five participating respondent groups in the 2022 American Dental Education Association Climate Study. RESULTS: On average, all groups agreed/agreed strongly that they were culturally competent (5-point Likert scale with 5 = strongly agree: all means > 4.50). However, the mean responses of students and staff were significantly less positive than the responses of faculty, administrators, and admin/faculty (Means: 4.51/4.54/4.72/4.64/4.74; p < 0.001). While students' subjective cultural competency-related responses were positive (Mean = 4.10), objective responses related to participation in six cultural competence activities were low (0-6 possible "Yes" responses: Mean = 1.81). Students' mean response to nine questions concerning whether they had increased their skills/abilities related to treating patients from different backgrounds was also low (0-9 possible "Yes" responses: Mean = 3.86). The mean satisfaction of the five groups differed, with staff being least satisfied and admin/faculty being most satisfied (Means: staff = 3.68/students = 3.84/faculty = 3.96/administrators = 3.92/admin/faculty = 4.07; p < 0.001). Overall cultural competence-related responses correlated with climate-related satisfaction (r = 0.27; p < 0.001). Students' cultural competency correlated with their self-reported increased clinical skills (r = 0.28; p < 0.001) and intended future support of their school/program (r = 0.52; p < 0.001). CONCLUSIONS: Members of dental schools and allied dental education programs consider themselves culturally competent and are satisfied with the cultural climate in their workplace. The more culturally competent they are, the more satisfied they are, and the more likely students are to become supportive alumnae/i and the more committed faculty members are to remain in their jobs.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".