Modelling the Maternal Oral Health Knowledge, Age Group, Social-Economic Status, and Oral Health-Related Quality of Life in Stunting Children
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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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".