Dental Student Enrollment and Graduation: A Report by State, Census Division, and Region
Bibliographic record
Abstract
The purpose of this study is to provide descriptive data on the presence of dental schools, dental school graduates, instate enrollment, and interstate dental education agreements for U.S. states, districts, and regions. This information may be helpful in deciding to open or maintain a dental school. Data from the American Dental Association (ADA), American Dental Education Association (ADEA), and U.S. Census Bureau were used to conduct cross-sectional comparisons for states, census divisions, and regions for 2000. In 2000, there were fifty-four dental schools in thirty-two states and the District of Columbia. Total graduation across 1990-2000 was 43,289 dentists. Over half (56 percent) of the graduates were from public schools. The distribution of schools and graduates differed by geographic region. Alaska, Utah, Hawaii, and Nebraska were outliers with respect to high and low numbers of dental schools in states, in-state enrollment, and dentists to population. U.S. states, districts, and regions vary widely on the number of dental schools, dentists to population, first-year dental school enrollees, and dental school graduates. Further assessment on additional factors such as dental health provider shortage areas, state oral health status, and attractiveness of locations to dentists is needed to more fully understand the impact of these factors.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".