Modelling the Maternal Oral Health Knowledge, Age Group, Social-Economic Status, and Oral Health-Related Quality of Life in Stunting Children
Why this work is in the frame
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Bibliographic record
Abstract
The main themes are two main health problems affecting children under five in Indonesia, namely nutrition and oral health. Lack of nutrition in children can also affect their general health, and so does their oral health, leading to their quality of life. The study aimed to analyse the relationship between maternal oral health knowledge, maternal age group, social-economic status with the oral health-related of life in stunting children. This type of analytical research used a survey method on 86 mothers aged 2-5 years in one of 15 villages designated by the mayor of Bandung as a stunting locus. Maternal oral health knowledge, social-economic status, and oral health-related quality of life were assessed using a set of questionnaires that have been pre-tested to non-participant mothers. The hypotheses of the conceptual model were tested using structural equation modelling-partial least squares. The results showed that 16.7% of the variance in OHRQoL was explained by maternal oral health knowledge and the maternal age group. Social-economic status has an indirect relationship to OHRQoL by predicting the maternal oral health knowledge 10.6%. The path coefficient between maternal age group and OHRQoL was the strongest (b = -0.350, P = 0.000), followed by SES and maternal oral health knowledge (b = 0.325, P = 0.04) and to OHRQoL (b = 0.215, P=0.02). The overall predictive power of the model was 10.6%. This result indicated maternal oral health knowledge, social-economic status, and maternal age group related to children's oral health quality of life.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it