“We talk teeth”: Exploring faculty EDIA (equity, diversity, inclusivity, and access) capacity in dental education
Bibliographic record
Abstract
PURPOSE: There are increasing concerns regarding inequitable educational access and experiences for underrepresented populations in health education, prompting dental faculties to recognize EDIA (equity, diversity, inclusivity, and access) capacity as a strategic priority. Faculty members contribute to the establishment and reinforcement of institutionally engrained norms within learning settings with significant influence on the experience of students. Currently, there is limited literature on faculty EDIA capacity within dental education and minimal evidence to inform barriers to development. This study sought to explore how dental faculty members perceive their personal and institutional EDIA capacity and to identify current strengths and weaknesses of EDIA development within the institution of study and dental education. METHODS: Using a hermeneutic study design, semi-structured interviews were conducted on a convenience sampling of dental faculty members (n = 10) and a thematic, interpretative analysis was applied. RESULTS: Findings revealed six dominant themes impacting EDIA capacity. Knowledge of EDIA language, interfaculty communication, and institutional messaging are identified as weaknesses, whereas informal, community building events for EDIA development are identified as novel strengths meriting prioritization. Motivation to engage in EDIA by faculty members overall is illuminated in relation to emotionally provocative experiences. CONCLUSION: Current institutional communication of EDIA is unconsciously restricting capacity building based on hierarchical and prescribed parameters. Developing capacity in dental education requires a redirection of resources to initiatives valuing social bonding over prescribed box-checking. This study reveals a new narrative of EDIA capacity within dental education and sustainable pathways for development with high transferability to other health programs.
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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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| 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".