ORAL HEALTH IN LATER LIFE: RESEARCH CHALLENGES, OPPORTUNITIES, AND INNOVATIONS
Bibliographic record
Abstract
One of the most serious yet under-researched concerns among older adults is oral health. Mounting population-based research demonstrates stark socioeconomic, ethnic, and regional disparities in service utilization, edentulism, and untreated decay. Practitioners have identified poor quality oral health among long-term care residents, due in part to lack of training among staff and family caregivers. This panel brings together population health researchers and practitioners to identify the challenges in studying and treating oral health among older adults in the U.S. and Canada. Wu and Luo describe oral health disparities in the United States. Using National Health Interview Survey data, they also reveal vast heterogeneity in the dental health of Asian Americans, and delineate the role of language and cultural factors in explaining these disparities. Zwetchkenbaum and Carr provide an overview of large longitudinal data sets in the United States that are well-suited to studying oral health and its psychosocial correlates over the life course, and recommend future data collection initiatives. Yoon and Hoben describes gaps in research and assessment of the oral health concerns of long-term care residents, and present newly developed, valid assessment tools. They present plans for the development of an oral health think tank that will address research priorities identified by key stakeholders. Agha & Lyford describes the philosophies and practices of a holistic dental clinic in the United States, and documents the efficacy of this innovative care coordination model for enhancing late-life oral health. Panelists will propose new research and practice partnerships to further address late-life oral health concerns.
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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 | 0.161 | 0.123 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.015 | 0.039 |
| Open science | 0.008 | 0.014 |
| Research integrity | 0.011 | 0.017 |
| Insufficient payload (model declined to judge) | 0.007 | 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".