Geriatric dentistry education and context in a selection of countries in 5 continents
Bibliographic record
Abstract
PURPOSE/AIM: To summarize and discuss how geriatric dentistry has been addressed in dental schools of different countries regarding to (1) teaching students at the predoctoral level; (2) advanced training, and (3) research. METHOD AND MATERIALS: A convenience sample of faculty members from a selection of high, upper-middle and lower-middle income countries were recruited to complete the survey. The survey had 5 open-ended main topics, and asked about (1) the size of their elderly population, (2) general information about dental education; (3) the number of dental schools teaching geriatric dentistry, and their teaching methods; (4) advanced training in geriatric dentistry; (5) scholarship/research in geriatric dentistry. RESULTS AND CONCLUSION: (1) There is great variation in the size of elderly population; (2) duration of training and content of dental education curriculum varies; (3) geriatric dentistry has not been established as a standalone course in dental schools in the majority of the countries, (4) most countries, with the exception of Japan, lack adequate number of dentists trained in geriatric dentistry as well as training programs, and (5) geriatric dentistry-related research has increased in recent years in scope and content, although the majority of these papers are not in English.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".