A Tool for Assessing Cultural Competence Training in Dental Education
Bibliographic record
Abstract
Policies exist to promote fairness and equal access to opportunities and services that address basic human needs of all U.S. citizens. Nonetheless, health disparities continue to persist among certain subpopulations, including those of racial, ethnic, geographic, socioeconomic, and other cultural identity groups. The Commission on Dental Accreditation (CODA) has added standards to address this concern. According to the most recent standards, adopted in 2010 for implementation in July 2013, CODA stipulates that "students should learn about factors and practices associated with disparities in health." Thus, it is imperative that dental schools develop strategies to comply with this addition. One key strategy for compliance is the inclusion of cultural competence training in the dental curriculum. A survey, the Dental Tool for Assessing Cultural Competence Training (D-TACCT), based on the Association of American Medical Colleges' Tool for Assessing Cultural Competence Training (TACCT), was sent to the academic deans at seventy-one U.S. and Canadian dental schools to determine best practices for cultural competence training. The survey was completed by thirty-seven individuals, for a 52 percent response rate. This article describes the use of this survey as a guide for developing culturally competent strategies and enhancing cultural competence training in dental schools.
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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 | 0.013 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".