Social & Behavioral Risk Factors and Early Childhood Caries – A Cross-Sectional Study on Preschool Children in Shah Alam
Bibliographic record
Abstract
Introduction: Early childhood caries (ECC) remains a major challenge among the 5-6 year olds in Malaysia with a caries prevalence of 71.3% as indicated in the last survey of preschool children in 2015. Studies have shown that behavior and income status can influence development of ECC. Objectives: The aim of this study was to measure the caries prevalence among 2 – 5 years old preschool children and to study the association of parents’ socio behavioral factors on ECC. Materials and methods: 140 preschool children participated in this study. Parents were given a set of structured questionnaires and oral examination was conducted on their children after receiving consent. Results: Findings showed prevalence of dental caries was 50.1% with mean dft score of 2.51. There was significant association between dental caries and children drinking formula milk and sweet drinks in their bottles: (p<0.05). Children from lower income family and lower education level have significantly higher caries prevalence compared to those from more privileged family. Conclusions: Drinking pattern, family income and education level appear to be contributing factors towards development of ECC among this group of children. It is recommended that health promotion interventions be targeted towards the lower income group with emphasis on drinking pattern of the children.
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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 | 0.000 |
| 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 | 0.000 | 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".