Potensi Kader Posyandu sebagai Tenaga Skrining Early Childhood Caries (ECC) Tidak Terawat
Bibliographic record
Abstract
Background: Early Childhood Caries (ECC) is a significant health problem. Untreated ECC can have substantial impacts on individuals and communities, causing pain, disrupting functions, hindering the child's growth process, affecting the child's weight, and influencing the child's developmental progress, ultimately reducing the quality of life for the child. Preventive efforts for untreated ECC include activities such as ECC screening, allowing for prompt intervention in cases of untreated ECC. Non-dental health personnel, such as community health workers, are alternative resources that can be empowered to participate in screening for untreated ECC using pufa Index. Purpose: to analyze the level of validity and reliability of community health workers in conducting screening for untreated ECC using the pufa index. Methods: The research design involves diagnostic testing with a cross-sectional approach, and samples are taken from children in community health posts using simple random sampling. The study is conducted in the working area of the Sijunjung Health Center, Sijunjung District, West Sumatra. Results: The reliability of cumulative PUFA examinations by Community health workers with a Kappa value of 0.88 is considered a very strong level of agreement. The sensitivity and specificity values of cumulative PUFA are 96% and 94%, respectively, which indicate excellent diagnostic values. This means that Community health workers demonstrate a very strong level of agreement and excellent accuracy in diagnosing untreated ECC using the PUFA index in young children. Conclusion: Community health workers have great potential to be empowered as personnel for screening untreated ECC using pufa index.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".