Do socio-economic disparities in dental treatment needs exist in Lithuanian adolescents?
Bibliographic record
Abstract
OBJECTIVE: To explore disparities in needs for dental treatment which arise from individual and area-based socio-economic determinants. RESEARCH DESIGN: A cross-sectional study conducted in 22 randomly selected Lithuanian areas. SETTING: In each of the pre-selected areas, one secondary school was randomly chosen. PARTICIPANTS: A total of 885 15-16-year-olds participated. Outcome measures. Dental treatment need was evaluated following the WHO guidelines and aQuantitative Summative Dental Treatment Needs Index (QSDTNI) was used to calculate the total burden of needs. The information about socio-economical determinants was obtained from a structured questionnaire and national statistics database. Individual socio-economic status (SES) measures were: parents' occupation, family structure, family income and affordability to have holiday used as a proxy measure for income. The area-based SES estimates were: unemployment, average household income, educational attainment, natural increase/decrease of population in an area and net migration rate. Data was analyzed by bivariate and multivariate analyses. RESULTS: None of significant bivariate associations between individual socio-economic variables and the QSDTNI were detected. Among area-based variables natural increase/decrease of population in an area and net migration rate were significantly related to the QSDTNI. Two individual and two area-based factors were extracted and introduced into Linear Multiple Regression Analysis (LMR). The LMR model was significant, but only one factor, i.e. area demographics, significantly contributed to this model. CONCLUSION: There are no clear social disparities in dental treatment needs in Lithuanian adolescents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".