Impact of a Novel Irene Donuts Application on Maternal Behavior and Children's Dental Hygiene Status
Bibliographic record
Abstract
The Irene Donuts application is an interactive computer program that aims to make students aware of the risk factors for dental health.This program provides an understanding of caries risk factors early on and how to prevent caries, has a visual picture of caries risk, and empowers parents to maintain dental health, especially at school.This study aimed to determine the effect of the application of Irene donuts in family dental nursing care on changes in maternal behavior and children's dental hygiene status in Gampong Ateuk Jawo, Banda Aceh, Indonesia.The method that has been used in this study is quasi-experimental.The experimental design is an equivalent control group design with pre-test and post-test.This study's sample was 60 children and their mothers as respondents, divided into two groups, i.e., intervention and control.The results did not show any difference in the mean values of knowledge, attitudes, and actions of mothers and children dental and the oral hygiene status of children (PHP-M children) before the intervention in the treatment group, and a control group which was statistically significant.Statistically, there is a difference in knowledge, attitudes, and actions of mothers and dental and PHP-M children immediately after and two weeks after the intervention between the treatment group and the control group.Finally, from this study is families should improve their children's dental and oral health, it is necessary to increase knowledge, attitudes, and positive practices of mothers as provisions in educating children by giving examples to their children on how to maintain dental health.
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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 | 0.000 | 0.001 |
| 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) | 0.005 | 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".