International education research issues in meeting the oral health needs of geriatrics populations: an introduction
Bibliographic record
Abstract
In 2005, the World Health Organization outlined priorities for geriatric oral health and recommended education for oral health care providers in both biomedical and psychosocial aspects of care for older people.1 In order to reassess the existing educational systems and training needs of those serving increasingly elderly societies, this topic was made the focus of two international symposia held in 2008—the first at the American Association for Dental Research (AADR) Annual Meeting in Dallas2 and the second at the International Association for Dental Research (IADR) General Session in Toronto.3 Revised versions of the papers presented by some of the speakers who contributed to these meetings are being published here. The overall purpose of the symposia was to propose a research framework for use in the development of educational practice. The specific aims were to: (1) describe current practice in the education and training of those serving the oral health needs of the geriatric population; (2) seek understanding of the relationship of educational systems to the oral health status, needs, and demographics of the geriatric population, exploring cross-national differences; and (3) consider the challenges and opportunities for educational research to improve the oral health and promote the well-being of the geriatric population. This introduction provides a summary of the three principal themes that emerged from the meetings: defining older adults, geriatric dental education, and research issues. The introduction concludes with suggested dimensions that might be included in a research framework.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 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".