Relationship of History of Exclusive Breastfeeding with Stunting Incidence in Toddlers 24 – 59 Months in the Marawola Health Center Work Area
Bibliographic record
Abstract
Salah satu masalah kesehatan yang mengancam anak Indonesia bahkan dunia adalah stunting. Prevalensi kejadian stunting di Sulawesi tengah sebesar 21,4% dimana Kabupaten Sigi berada diurutan tertinggi ke tiga dengan prevalensi stunting 24,6%, setelah Kabupaten Donggala 34,9% dan Kabupaten Tojo Una-una 26,6%. Sebanyak 63 balita dari 463 balita usia 24-59 bulan mengalami stunting diwilayah kerja Puskesmas Marawola Kecamatan Sigi. Penelitian ini bertujuan untuk mengetahui hubungan riwayat pemberian ASI eksklusif dengan kejadian stunting pada balita usia 24-59 bulan di Wilayah Kerja Puskesmas Marawola Kabupaten Sigi. Jenis penelitian analitik dengan rancangan penelitian case control. Populasi dalam penelitian ini adalah balita stunting dan tidak stunting usia 24-59 bulan Teknik pengambilan sampel menggunakan metode proporsional random sampling. Sampel sebanyak 39 balita stunting dan 39 balita tidak stunting jadi total sampel sebanyak 78 balita berusia 24-59 bulan. Analisa data menggunakan uji Chi Square. Ada hubungan riwayat pemberian ASI eksklusif dengan kejadian stunting pada balita usia 24-59 bulan di wilayah kerja puskesmas marawola kabupaten sigi dengan nilai P=0,023 (P<0,05) nilai OR=2,875. Riwayat pemberian ASI eksklusif berhubungan dengan kejadian stunting pada balita usia 24-59 bulan diwilayah kerja Puskesmas Marawola Kecamatan Sigi. Perlu dilakukan pemberian edukasi tentang pentingnya pemberian ASI ekslusif untuk pecegahan stunting.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".