Prevalence of and factors associated with dental service utilization among early elderly in Lithuania
Bibliographic record
Abstract
BACKGROUND: There is no recent information about dental service utilization (DSU) among elderly in Lithuania. We examined DSU and its associated factors in Lithuanian early elderly based on the Andersen's behavioural model. METHODS: The cross-sectional study conducted in 2017-2019 included a nationally representative stratified sample of 370 Lithuanian early elderly aged 65-74 years (response rate of 54.5%). Information on predisposing factors (age, sex, nationality and education), enabling factor (residence), need-based factors (status of teeth, oral pain or discomfort, and dry mouth), general health, personal health practices and perceived stress was obtained from a structured, self-administered questionnaire. Clinically-assessed need-based factors included number of missing teeth and dental treatment need. Multivariable Poisson regression with robust variance estimates was used. RESULTS: A total of 239 study participants (64.6%) reported a dental visit during the last year and 338 (91.4%) needed dental treatments. A higher level of education (adjusted prevalence ratio [aPR] = 1.21, 95% confidence interval [CI]:1.04-1.40), pain or discomfort in teeth/mouth (aPR = 1.35, 95%CI: 1.13-1.62) and lower number of missing teeth (aPR = 0.99, 95%CI: 0.98-1.00) were associated with DSU. CONCLUSIONS: Even though majority of early elderly needed dental treatments, only two-thirds visited a dentist during the last year. Predisposing and need-based factors were significant predictors of having a dental visit in the last year. A national oral health program for Lithuanian elderly with the focus on regular preventive dental check-ups is needed. More studies, both quantitative and qualitative, are warranted to investigate in depth the barriers for DSU among elderly in Lithuania.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".