A Theory-Based Digital Intervention to Improve Maternal Oral Health Behaviors for Young Children: Quasi-Experimental Study
Bibliographic record
Abstract
Abstract Background Parental oral health education is critical for preventing early childhood caries. However, few interventions are theoretically grounded or use digital approaches. Objective The objective of this study was to evaluate the effects of a health belief model–based digital intervention on maternal oral health behaviors. Methods This quasi-experimental study enrolled 648 mother-child dyads from 19 community health care centers (CHCs) in Beijing, China. CHCs were allocated to intervention or control groups depending on their voluntary adoption of the dental referral system. Ten CHCs (n=332, 52.6%) were assigned to the intervention group, where mothers received oral health education materials and had access to a dental referral system. The remaining 9 CHCs (n=316, 47.4%) served as the control group, in which mothers continued to receive standard child health care services. The primary outcome was parent-assisted toothbrushing, and the secondary outcome included other oral health behaviors, including night feeding practices, sugar intake, and dental visits. To evaluate the intervention effects on behavioral outcomes, generalized linear mixed models were used, accounting for repeated measures and potential confounding factors. Results Compared with the control group, the intervention group demonstrated a significant increase in parent-assisted toothbrushing, with an absolute difference of 10.3 (95% CI 3.0 to 17.6; P =.006) percentage points at 6 months and 1.5 (95% CI −7.2 to 10.1; P =.74) percentage points at 12 months. Additionally, dental visit rates were significantly higher in the intervention group at 12 months (odds ratio 4.65, 95% CI 1.30 to 16.70; P =.02). However, no statistically significant differences were observed between groups in nighttime feeding cessation or sugar intake control at either the 6- or 12-month follow-ups. Conclusions The health belief model–based digital intervention was effective in the short term for enhancing parent-assisted toothbrushing in young children, but its long-term effectiveness remains unproven. Future research should therefore prioritize exploring sustainability strategies.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".