Oral health in America 2021: Making a case for curricular change
Bibliographic record
Abstract
BACKGROUND: The NIH Oral Health in America: Advances and Challenges report is the most recent evidence-based review of the status of oral health in North America since Oral Health in America: A Report of the Surgeon General, which was published in 2000. This article aims to synthesize and discuss information from the report pertinent to improving dental education to positively impact oral health. Calls for action and suggestions for implementation are presented. METHODS: The authors reviewed each section from the report and identified key messages relevant to dental education. These were then combined into a framework based on the NIH report's three main "call to action" items. A matrix for calls to action and implementation recommendations was developed using the findings from the 2021 NIH report and a previous 2018 report on Advancing Dental Education in the 21st Century. CONCLUSION: The information discussed in the report related to dental education has the potential to improve oral health, and educators, schools, professional organizations, state, and federal agencies are called to develop and/or implement action plans focused on curriculum, competencies, workshops, guidelines, and policies based on the summary framework presented in this study.
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 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 | 0.026 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".