Hubungan Tingkat Pengetahuan dengan Sikap Ibu Hamil Tentang Inisiasi Menyusui Dini (Imd) di Puskesmas Kasreman Kabupaten Ngawi
Bibliographic record
Abstract
Inisiasi menyusui dini merupakan menyusui yang dilakukan satu jam pertama setelah lahir sampai proses awal berakhir. Data World Health Organization (WHO) menyatakan angka kematian bayi (AKB) pada tahun 2018 sebesar 28,9 per 1000 kelahiran hidup. Negara Afrika menepati urutan pertama kematian neonatal dengan pravelensi 51,8 per 1000 kelahiran hidup, diikuti oleh Eastern Mediterranean 37,2 per 1000 angka kelahiran hidup, dan South-East Asia 27,6 per 1000 angka kelahiran. Tujuan penelitian : untuk mengetahui hubungan tingkat pengetahuan dengan sikap ibu hamil tentang inisiasi menyusui dini di Kecamatan Kasreman. Metode penelitian : Desain yang digunakan dalam penelitian adalah desain korelasi dengan pendekatan cross sectional dengan consecutif sampling. Jumlah sampel 30 dengan katagori inklusi dan eksklusi. Analisa univariat menggunakan statistic deskriptif dan analisa bivariat menggunakan uji chi square. Hasil penelitian : Di dapat bahwa responden penelitian ini terbanyak memiliki pengetahuan baik dan memiliki sikap positif tentang IMD, yaitu sebanyak 17 responden (56,7%) memiliki pengetahuan baik dan 12 responden (40,0%) memiliki sikap positif. Dari uji statistic chi square di peroleh ρ = 0,002 berarti H1 diterima atau terdapat hubungan antara tingkat pengetahuan dengan sikap ibu hamil tentang inisiasi menyusui dini. Kesimpulan : Ada hubungan tingkat pengetahuan dengan sikap ibu hamil tentang inisiasi menyusui dini (IMD)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".