Barriers to dental visits in<scp>B</scp>elgium: a secondary analysis of the 2004<scp>N</scp>ational<scp>H</scp>ealth<scp>I</scp>nterview<scp>S</scp>urvey
Bibliographic record
Abstract
OBJECTIVE: The study aims to identify barriers to annual dental visits in the Belgian population. METHODS: We conducted a secondary analysis of data collected through the 2004 National Health Interview Survey in Belgium. Only respondents aged 15 years and older with complete information on dental consultations and the independent variables (n = 5940) were considered in this analysis. The associations between the lack of dental visits during the 12 months preceding the survey and covariates of interest were examined using a multivariable logistic regression analysis. RESULTS: Almost one-half of the respondents (49.7 percent) did not visit a dentist in the 12 months prior to the survey. Region of residence was significantly the common variable for the three age categories. In the 15- to 34-year-old category, males and two-person households were significantly less likely to report a dental visit during the 12 months preceding the survey. For the 35- to 54-year-old category, a low level of education was the covariate associated with the lack of dental visit. In the 55 years or older category, the factors associated with the lack of a dental visit in the 12 months prior to the survey were: male gender, low level of education, low household income, low weekly alcohol consumption, current smoker, and body mass index of ≥ 25 mg/kg(2). CONCLUSION: Barriers to dental visits in Belgium differ among age groups and are linked to personal and environmental factors. The findings confirm the existence of social health inequalities in dental visits among Belgian people.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".