Dental and allied dental students’ cultural climate‐related experiences and perceptions: How does ethnicity/race matter?
Bibliographic record
Abstract
OBJECTIVES: The cultural climate of an academic institution affects students' academic performance and well-being. The objectives were to investigate the culture-related experiences and perceptions of dental and allied dental students in the United States (US) and Canada who self-identified in response to a survey question as American Indian/First Nation-Indigenous/Hawaiian Native (AI), Black/African American/Canadian American/African (Black), Hispanic/Latinx (HL), Middle Eastern (ME), East Asian/Southeast Asian/South Asian (AA), White/European (White) or multiracial (M). The comparisons focused specifically on experiences of biases/inequities related to systemic factors (policies/practices) and personal discrimination/harassment, and on perceptions of the cultural climate and climate-related consequences, having a sense of belonging and culture-related clinical competence. Relationships between these constructs of interest were explored. METHODS: Descriptive and inferential statistics such as univariate analyses of variance and chi-square tests were used to analyze the data from 10,279 students who participated in the 2022 American Dental Education Association climate study and responded to the question concerning which academic program they attended. RESULTS: AI and Black students reported the highest mean numbers of experienced biases/inequities related to policies/practices, while White students experienced the lowest mean (Range of sum scores: 0-16: AI:5.06/Black:4.38/ME:4.11/M:3.96/HL:3.54/AA:3.47/White:3.06; p < 0.001). The mean sum score of having witnessed and experienced harassment and discrimination was highest for Black and lowest for HL students (Range: 0-4: Black:0.82/M:0.73/ME:0.66/AA:0.6/White:0.46/AI:0.40/HL:0.36; p < 0.001). Significant differences in mean responses were also found for general climate perceptions (5-point answer scale with 5 = most positive: ME:3.58/Black:3.59/M:3.59/AA:3.66/White:3.73/AI:3.77/HL:3.87; p < 0.001), personal climate-related consequences (ME:3.70/BM:3.72/Black:3.73/AA:3.75/AI:3.83/White:3.85/HL:3.96; p < 0.001), having a sense of community (ME:3.84;Black:3.87/AI:3.88/BM:3.93/AA:3.95/HL:4.09/White:4.11; p < 0.001) and the "Culturally competent clinical care" Index (AA:4.30/M:4.32/AI:4.34/Black:4.37/E:4.40/White:4.43/HL:4.54; p < 0.001). The higher the witnessed/experienced harassment/discrimination was, the less positive the students perceived the general climate (r = -0.52; p < 0.001), their own situation (r = -0.51; p< 0.001), their sense of belonging (r = -0.43; p < 0.001) and cultural competence when providing clinical care (r = -0.24; p < 0.001). CONCLUSIONS: Students' ethnic/racial background matters. It affects their perceptions of the school/program climate, their experiences of biases/inequities related to policies/practices and the degree to which they experience and witness harassment and discrimination. These findings should be a wakeup call for faculty and administrators to progress on fulfilling the CODA requirements to create a humanistic environment for all students.
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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 | 0.003 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".