Children's perception of caries and gingivitis as determinants of oral health behaviours: a cross‐sectional study
Bibliographic record
Abstract
AIM: To evaluate the relationship between children's perception of caries and gingivitis and their oral health behaviours. DESIGN: Participants in this cross-sectional study were children aged 11-14 years. A questionnaire for measuring children's perceptions and behaviours was developed, validated and applied. Perceptions were analysed as predictors for behaviours using multiple logistic regression analysis. RESULTS: A total of 434 children (57% males) participated in the study. Half of them perceived caries as a disease and believed in visiting the dentist regularly regardless of dental need. More than 60% were unaware that gum bleeding is a sign of disease and only 60.7% believed that it requires a management. Being aware that gum bleeding is a sign of disease and that it requires treatment increased the odds of brushing 2.83 (OR = 2.83, 95% CI:1.33-6.12) and 2.1 (OR = 2.1, 95% CI:1.05-5.55) times, respectively. Children aware of importance of dental visits even without dental decay were 2.9 times more likely to visit the dentist regularly (OR = 2.86, 95% CI:1.25-5.75) and were 77% more likely to never miss a dental appointment (OR = 1.77, 95% CI:1.03-3.37). CONCLUSION: Being aware that bleeding gum requires treatment was a determinant of toothbrushing habit. Improved perceived need for dental check-up regardless of dental problem may promote children's preventive dental attendance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".