The Pathway to Dentistry for Minority Students: From Their Perspective
Bibliographic record
Abstract
The small number of minorities in the field of dentistry is a serious concern. While the United States as a whole has become more diverse with minorities making up 25 percent of the total U.S. population, only a handful (14 percent) are currently practicing dentistry, and only 11 percent entering dental schools are underrepresented minorities. Pipeline, Profession, & Practice: Community-Based Dental Education is a national foundation-sponsored program designed to address this issue in dental education. To understand the reasons why dentistry attracts so few underrepresented minority (URM) students, we conducted focus groups and in-depth interviews to determine the challenges facing minority students when they apply to and attend dental school. Ten focus groups were conducted with a total of ninety-two minority students (fourteen undergraduate students and seventy-eight students currently enrolled in dental schools) at six universities in four geographic regions. In addition, four in-depth interviews were held with faculty advisors who teach, mentor, and recruit minority students. The major findings of the study are as follows: 1) early and frequent exposure to dentistry and dentists in practice is essential for minority students to consider this profession; 2) while many dental schools have earnestly tried to recruit minority applicants, most URM students find out about dental programs by a family member or friend and not as a result of an intentional recruiting effort; and 3) hearing directly from minority students could be a solid first step in understanding the dental school experience from a different vantage point. This study has important implications for the methods dental schools use to both recruit minority students and foster a learning environment that is sensitive to students from diverse backgrounds.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.006 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.009 | 0.018 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".