Penguatan Kapasitas Manajemen Data Berbasis Digital Health untuk Pencegahan Stunting
Bibliographic record
Abstract
ABSTRAK Stunting merupakan masalah kurang gizi kronis yang disebabkan oleh kurangnya asupan bergizi dalam jangka waktu lama. Stunting menjadi masalah serius karena berhubungan dengan risiko kegagalan pertumbuhan dan perkembangan anak. Secara tidak langsung hal tersebut akan mempengaruhi produktivitas ekonomi suatu negara. Prevalensi stunting di Kabupaten Sukoharjo tahun 2022 sebesar 19,8%. Angka tersebut belum memenuhi target Rencana Pembangunan Jangka Menengah Nasional (RPJMN) tahun 2024 sebesar 14%. Upaya penurunan angka stunting memerlukan kerjasama dari berbagai pihak. Salah satunya melalui penguatan kapasitas kader posyandu dan orangtua. Posyandu Dahlia merupakan salah satu posyandu yang berada di wilayah Kecamatan Bendosari, Sukoharjo. Manajemen data kesehatan yang dilakukan di posyandu sebatas pencatatan berat badan dan tinggi badan tanpa dilakukan interpretasi dan analisis data. Selain itu pengetahuan kader dan orang tua terkait stunting juga belum baik. Kegiatan pengabdian bertujuan meningkatkan kapasitas kader posyandu dan orangtua terkait manajemen data kesehatan dengan memanfaatkan aplikasi berbasis digital health. Kegiatan pengabdian dilakukan melalui focus group discussion dan pelatihan penggunaan aplikasi pemantauan pertumbuhan dan perkembangan anak berbasis digital health. Setelah dilakukan kegiatan pengabdian kepada masyarakat, terdapat beberapa perubahan pada mitra yang dilihat dari peningkatan pengetahuan terkait stunting dengan nilai rata-rata pretest 47,06 menjadi nilai rata-rata posttest 86,47 serta kader posyandu dan orangtua mampu melakukan pemantauan pertumbuhan dan perkembangan anak menggunakan aplikasi berbasis digital health. Kata Kunci: Digital Health, Manajemen Data, Posyandu ABSTRACT Stunting is a chronic malnutrition problem caused by a long-term lack of nutritious intake. Stunting is a serious problem because it is associated with the risk of failure in growth and development of children. Indirectly this will affect economic productivity of a country. The prevalence of stunting in Sukoharjo Regency is 19.8%. This number does not achieve RPJMN 2024 target as many as 14%. Stunting reduction require collaborative work at all levels. Strengthening capacity of posyandu cadres and parents is one way to prevent stunting. Posyandu Dahlia is one of the posyandu in Bendosari District, Sukoharjo. Health data management at posyandu was limited to data collection without data interpretation and analysis. Cadres’ and parents’ knowledge about stunting was not enough. This community empowerment aimed to increase capacity of posyandu cadres and parents for stunting prevention by utilizing digital health-based applications. This program were carried out through focus group discussions and use of digital health-based child growth and development monitoring applications training. This community empowerment results was increase posyandu cadres’ and parents’ knowledge about stunting with average pretest score 47.06 to average posttest score 86.47. In addition, posyandu cadres and parents can monitor growth and development using digital health-based applications. Keywords: Digital Health, Data Management, Posyandu
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.111 | 0.027 |
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".