School-Based Dental Education for Improving Oral Self-Care in Mexican Elementary School–Aged Children
Bibliographic record
Abstract
Aim. To test the efficiency and acceptance of school-based dental education for improving oral self-care in Mexican elementary school–aged children. Method. A total of 408 students from 4 schools were examined at the baseline, 3 months (follow-up rate was 94%) and 6 months observations (follow-up rate 91%). Group 1 served as a control, Group 2 received a lecture-based education, and Group 3 consisted of trained peer-leaders who educated their peers. Oral self-care practice and oral self-care skills were assessed at the baseline and both follow-ups. A number of sociodemographic and oral health behavior/knowledge characteristics were tested as predictors of oral self-care outcomes at different observation periods. Results. Oral self-care outcomes improved in Groups 2 and 3, but not in the control group. The selected child population, their caregivers and teachers perceived the school-based educational strategy as important and necessary. There was no consistent pattern of predictors explaining variations in oral self-care outcomes at any of the observation periods. Oral self-care improvement observed at the 6 months observation was mainly predicted by the baseline oral self-care levels, dental education, and age. Conclusions. The school-based dental education was easy to implement, and it was effective for improving children’s oral self-care practice and skills.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".