Development and Feasibility Testing of <scp>CATCH</scp> Healthy Smiles, an Oral Health Promotion Intervention for Prevention of Dental Caries Among Elementary School Children
Bibliographic record
Abstract
BACKGROUND: We present results of the development and feasibility testing of CATCH Healthy Smiles, a school-based oral health program, among children in grades K-2 in Houston, Texas. METHODS: Study design was cross-sectional (N = 2 schools; N = 125 parent-child dyads; 31 kindergarteners, 42 first graders, and 52 second graders). CATCH Healthy Smiles program was implemented by trained school teachers in the 2016-2017 school year. Trained dentists conducted dental assessments to measure dental caries increment score (d3mfs). Parent-reported 24-hour dietary recalls and surveys assessed child and parent behavioral, environmental, and psychosocial factors. Logistic regression analysis assessed factors associated with caries experience adjusting for covariates. RESULTS: Of the 113 children with complete dental assessments, 54% children in grade K, 62% in first grade, and 73% in second grade had caries experience. Children with caries experience had a higher body weight (AdjOR = 1.13, 95% confidence interval [CI]: 1.02-1.29), were less likely to be girls (AdjOR = 0.22, 95% CI: 0.05-0.82), had greater odds of difficulty drinking hot or cold beverages because of dental problems (AdjOR = 13.13, 95% CI: 1.09-275.14), greater frequency of consuming sugar-sweetened beverages (AdjOR = 11.53, 95% CI: 2.10-87.19), greater odds of receiving government assistance (AdjOR = 14.62, 95% CI: 2.74-119.81), and lower odds of seeing a dental provider (AdjOR = 0.11, 95% CI: 0.02-0.45). Process evaluation showed that 100% of the CATCH Healthy Smiles lessons and activities were taught in the two schools with a high degree of program fidelity and acceptability across the schools, children, and parents. CONCLUSIONS: These data will be used to conduct a subsequent fully powered cluster randomized controlled trial.
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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 | 0.009 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".