Ethnic and racial minority students in U.S. entry-level dental hygiene programs: a national survey.
Bibliographic record
Abstract
PURPOSE: The United States is rapidly becoming a more multicultural society. Although minority groups are the fastest growing segment of the U.S. population, minorities are not pursuing careers in health care professions in the same proportions. The literature suggests that increasing the number of minorities in the health care professions will increase access to health care for minority populations and help non-minority health care professionals become more aware of and sensitive to minority issues. The results reported here are part of a larger national survey that examined the commitment of entry-level dental hygiene programs to ethnic/racial diversity. METHODS: A 19-item survey was mailed in 1998 to all 233 entry-level dental hygiene program directors in the United States. The survey was piloted using a random sample of six entry-level dental hygiene program directors in the United States. Data were collected on demographics, formal written mission statements that support ethnic/racial diversity, minority recruitment programs, and recruitment mechanisms. Data were analyzed using frequencies, chi-square, t-tests, F-tests and Pearson correlation coefficients. The response rate was 60.1% (140). RESULTS: Results indicate that 10.5% of dental hygiene students and 6.7% of dental hygiene faculty are members of ethnic/racial minorities. Results also indicate that Asian and Pacific Islander (API) students are not underrepresented in U.S. entry-level dental hygiene programs, but Asian and Pacific Islander faculty are. CONCLUSIONS: A statistically significant relationship was found between the percentage of 1) minority faculty and students in entry-level dental hygiene programs; and 2) minority students and minorities in the state where the entry-level dental hygiene program resides.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".