Efektivitas Tata Kelola Penanganan Anak Stunting oleh Dokter dan Tenaga Kesehatan di Kota Yogyakarta
Bibliographic record
Abstract
The governance management of stunting children in Yogyakarta is carried out through the Stunting Case Audit (AKS) program. This program was implemented as an effort to meet the zero stunting target of Yogyakarta City through early intervention to reduce and prevent the appearance of similar cases from the audited areas. To find out that the AKS program has succeeded in achieving its goals, it is necessary to conduct a study to examine the effectiveness of the AKS program. This research is using a qualitative approach by comparing the suitability of program implementation with indicators of effectiveness. The results of this research explain that from the four effectiveness indicators used, only one indicator met the implementation of AKS. Indicators of timeliness have been met, while indicators of target accuracy, goal attainment and the emergence of expected results have not been met the expectation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".