Predictors of Dental Care Utilization in School Children in Al-Madinah, Saudi Arabia
Bibliographic record
Abstract
Aim: To explore the factors influencing dental care utilization including sociodemographic characteristics, and oral health need in 9-12-year-old school children in Al-Madinah, Saudi Arabia (SA).Methods: A stratified random sample was applied to select 10 schools in Al Madinah, SA and a total of 1000 students aged 9-12 years were included in the study.Information on sociodemographic factors and dental care utilization were collectedusing and oral health related quality of life was recorded using the World Health Organization (WHO) questionnaire.A multiple logistic regression model was used to examine the factors associated with dental care utilization.Results: Almost a quarter of all participants (23.8%), have never received dental care before.Pain or trouble with teeth was the most common reason for visiting the dentist (49.4%), while only 11.8% visited the dentist for routine check-up.Thepercentages of both missing school, and difficulty in eating due to oral health problems, were significantly higher among those who received dental care.Children from low-income families had a reduced likelihood of receiving dental care relative to children from higher and middle-income families (OR=0.571,P=0.014).Children who have caries and who reported having toothache in the past 12 months were more likely to visit the dentist (OR=1.599,P=0.028) &(OR=2.188,P>0.001).Conclusion: Dental care utilization is primarily driven by symptomatic dental care.The prevalence of dental utilization was relatively high among children from high-and middle-income families, children who have caries and children who reported having toothache in the past 12 months.
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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 | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".