Dental service utilization by Europeans aged 50 plus
Bibliographic record
Abstract
OBJECTIVES: To describe variations in the utilization of dental services by persons aged 50+ from 14 European countries and to identify the extent to which such variations are attributable to differences in oral health need and in accessibility of dental care. METHODS: We use data from the Survey of Health, Ageing, and Retirement in Europe (SHARE Waves 2 and 3) and estimate a series of multivariate logistic regression models to analyze variations in dental service utilization (overall dental attendance, preventive treatment and/or operative treatment, dental attendance in early life years) RESULTS: Overall dental attendance and incidence of solely preventive treatment are comparatively high in the Netherlands, Sweden, Denmark, Germany, and Switzerland. In contrast, overall dental attendance is relatively low in Spain, Italy, France, Greece, Poland, and Ireland. Moreover, a high incidence of solely operative treatment is observed in Austria, Italy, and France, whereas in the Netherlands, Sweden, Denmark, Switzerland, and Ireland, the incidence of solely operative treatment is comparably low. By and large, these variations persist even when controlling for cross-country differences in oral health need and in accessibility of dental care. CONCLUSIONS: In comparison with other European regions, there is a tendency toward more frequent and preventive dental treatment of the elderly populations residing in Scandinavia and Western Europe. Such utilization patterns appear only partially attributable to differences in need for and accessibility of dental care.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".