Equity-diversity-inclusion (EDI)-related strategies used by dental schools during the admission/selection process: a narrative review
Bibliographic record
Abstract
INTRODUCTION: Decades of evidence have demonstrated a lack of workforce diversity and sustaining disparities in academic dentistry and professional practice. Underrepresented minority students may face challenges and implicit bias during the dental schools' admission/selection process. This review collected papers from different countries to summarize the Equity-Diversity-Inclusion (EDI)-related strategies that dental schools worldwide have used in their admissions process to increase diversity. METHODS: A comprehensive search using MEDLINE (via PubMed), ERIC, Cochrane Reviews, Cochrane Trials, American Psychological Association Psyc Info (EBSCO) and Scopus was done between January and March-2023. All types of articles-designs were included, except comments and editorials, and all articles selected were in English. Two independent investigators screened the articles. Extracted data were general characteristics, study objectives, and EDI-related strategies. RESULTS: Sixteen publications were used to construct this manuscript. The year with the greatest number of publications was 2022. Type of studies were case studies/critical reviews (50%), cross-sectional (including survey and secondary data analysis) (n = 5, 31.25%), qualitative methods of analysis (n = 2, 12.5%), and retrospective/secondary data collection (n = 1, 6.25%). The strategies described in the articles were related to (1) considering the intersectionality of diversity, (2) using noncognitive indicators during the school admissions process to construct a holistic selection process, (3) diversifying, professionalizing, and providing training to admissions persons who had leadership roles with the support from the dental school and the university, and (4) allocating financial investments and analyzing current policies and procedures regarding EDI. CONCLUSIONS: This review aggregated interesting findings, such as: some schools are considering the intersectionality of diversity as a way to include underrepresented minorities and to diversify the students-body. The recent growth in publications on EDI during dental admission/selection process might indicate a positive movement in this field.
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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 | 0.018 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".