Faktor-Faktor Yang Berhubungan Dengan Status Karies Gigi Pada Anak Sekolah Min 1 Kota Banjarmasin
Bibliographic record
Abstract
Background of the study: At the age of 10 and over, 71,2% of children experience dental caries. The prevalence of active caries in Kota Banjarmasin is 65%. While in Puskesmas (Public health service) Kelayan Timur working area, 389 cases of dental caries found. Additionaly, the governement through Kemen-Kes RI (Indonesian Health Ministry) tergeting Indonesian society to be free from dental caries by 2030. Some factors influencing dental caries are dental health awareness and attitudes namely time and frequency of brushing teeth, cariogenic foods, and the method of brushing teeth. Purpose of the study: this study is aimed to reveal the correlation between knowledge factors as well as dental health attitudes and dental caries status. Methods: This is an anlytical study utilizing cross sectional. The sample of the study is students of MIN 1 Kota Banjarmasin in 2018 with the total of 53, taken by means of perposive sampling. Statistical testing used is chi-square with 95% of reliance degree. Result of the study: The result shows that there are correlations between dental health knowledge p-value 0,004, time and frequency of brushing teeth p-value 0,002, cariogenic foods p-value 0,018, as well as teeth brush method p-value 0,015 and dental caries occasion in MIN 1 Kota Banjarmasin. Conclusion: The dental caries status is affected by dental health knowledge and attitudes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".