Pemberdayaan Peran Kader Sebagai Penyuluh Menggunakan Kartu Edukasi IUFD Modifikasi Dwi Maryanti (IUFD MoBiDiti Card) Sebagai Antisipasi Kematian Janin Dalam Rahim Di Kelurahan Sidanegara Cilacap Tengah Tahun 2020
Bibliographic record
Abstract
Kematian janin dalam rahim (KJDR) berdasarkan riset penulis menunjukan bahwa kejadian BeratBadan Lahir Rendah (BBLR) memberikan risiko sebesar 101,36 kali untuk mengalami KJDR. Faktoryang berkontribusi terhadap berat badan lahir rendah yaitu kondisi anemia selama hamil, keadaanKurang Energi Kronis (KEK). Kejadian KEK dan anemia di Puskesmas Cilacap Tengah 1 diketahuisepanjang tahun 2018 dan 2019. Wilayah kecamatan Cilacap Tengah I mencakup 5 kelurahan, salahsatunya kelurahan Sidanegara. Kelurahan Sidanegara terdapat terdapat 23 RW dan pada setiap RWterdapat perwakilan 1 ketua kader. Luaran : IUFD MoBiDiti Card, HKI Karya Cipta dan PublikasiIlmiah. Tujuan dari pengabdian ini adalh meningkatkan pengetahuan dan keterampilan kader KIAtentang KJDR dengan menggunakan IUFD MoBiDiti Card sebagai antisipasi KJDR. Metodepelaksanaan dilakukan melalui kegiatan promosi dan preventif. Kegiatan pengabdian masyarakatmeliputi apersepsi, pengisian pre tes dan post tes serta pemberian materi KJDR dengan IUFDMoBiDiti Card. Pretes dan post tes berisi materi KJDR dan diberikan kepada sasaran yaitu kader KIA.Hasil pengabdian menunjukkan terjadi peningkatan pengetahuan berdasarkan rata-rata pre test danpost tes. Nilai rata-rata pre test 58 dan post test 73. Kesimpulan : IUFD MoBiDiti Card dapatmeningkatkan pengetahuan Kader KIA tentang KJDR.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.049 | 0.008 |
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".