Question · asked by 1 review

Theory-based oral health education/promotion interventions (HBM, TPB, SCT, HAPA, TTM, PMT, SOC, PRECEDE-PROCEED, MI) vs Control (waitlist, routine/standard oral health education, or minimal intervention): Mean DMFT (decayed, missing, filled permanent teeth)

Population
school‐age children and adolescents
Intervention
Theory-based oral health education/promotion interventions (HBM, TPB, SCT, HAPA, TTM, PMT, SOC, PRECEDE-PROCEED, MI)
Comparison
Control (waitlist, routine/standard oral health education, or minimal intervention)
Outcome
Mean DMFT (decayed, missing, filled permanent teeth)

Theory-based interventions showed no significant effect on DMFT scores (SMD -0.11; low certainty).

What each review found

Include earlier review versions
FindingEffectStudiesGRADE certainty
Theory-based oral health education/promotion interventions (HBM, TPB, SCT, HAPA, TTM, PMT, SOC, PRECEDE-PROCEED, MI) vs Control (waitlist, routine/standard oral health education, or minimal intervention)
DMFT scores (decayed, missing, filled teeth) · Children and adolescents aged 5-18 years · Post-intervention (follow-up 3 to 12 months, varies by study), School-based and community settings; multiple countries (predominantly Iran; also Pakistan, Hong Kong, Lithuania, Thailand), Overall
Theory-based interventions showed no significant effect on DMFT scores (SMD -0.11; low certainty).
Health promotion theory-based educational interventions for improving oral health in children and… (2025) All outcomes
SMD -0.11 [-0.27, 0.04]4 studies · 2556 participantsLow

Findings are shown side by side, never pooled across reviews.

The reviews

ReviewSearched up toIncluded studiesAMSTAR 2
Health promotion theory-based educational interventions for improving oral health in children and adolescents: a systematic review and…
2025 All outcomes
Up to March 2025 (searches conducted January to March 2025)36Critically Low / Critically Low
How reviews are matched to this question

Reviews word the same question differently. Four decision models (TypeSafe's Jev, Liquid AI's d1-ultra, SubQ Snap and Baseten's Elephant) judged each wording of the population, intervention, comparator and outcome against the others; two wordings count as the same when at least three of them agree and none clearly disagrees, and findings count as this question only when all four elements match. Timing, setting and subgroup never split a question: they are shown on each finding.