Awareness and Practice of Oral Health Measures in Medina, Saudi Arabia: An Observational Study
Bibliographic record
Abstract
The aim of this observational study is to investigate the oral health status and practices in the multicultural community of Medina, Saudi Arabia. A cross-sectional questionnaire was distributed that asked about oral health, dental and periodontal conditions, personal attitudes toward dental care, and smoking habits. Cross tabulation with chi-squared testing was carried out to investigate the association of toothbrush usage and smoking with several variables. Four-hundred and sixty subjects enrolled in the study. The majority of the respondents were students and Saudi males. More than 75% of the participants had neither a family dentist nor dental insurance; 7% were smokers, 84% used a toothbrush, 17% used dental floss and 34% used miswak (a teeth cleaning twig made from the Salvadora persica tree). Some of the individuals complained of tooth sensitivity, halitosis and bleeding gums. The main reason for dental visits was pain, with 23% of the participants having never visited a dentist. Tooth brushing was significantly associated with gender, nationality, occupation, education, marital status, having kids and dental insurance (p ≤ 0.05). Tobacco consumption was significantly associated with age, occupation, education level, marital status, having children, having bleeding gingivae and halitosis. Effective dental education programs are needed to improve dental knowledge and awareness in the Medina community.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".