Hubungan Antara Pengetahuan Ibu, Motivasi Ibu, Dan \nDukungan Bidan Dengan Kesediaan Ibu Melakukan \nInisiasi Menyusui Dini Di Wilayah Kerja \nPuskesmas Gajahan Kota Surakarta
Bibliographic record
Abstract
Menyusui sejak dini mempunyai dampak yang positif baik bagi ibu maupun \nbayinya. Ketidaktahuan dan kurangnya informasi tentang menyusui dini membuat ibu \nkurang termotivasi untuk melakukan inisiasi menyusui dini dan kurangnya dukungan \nbidan membuat ibu tidak percaya atau takut untuk melakukan inisiasi menyusui dini. \nPenelitian ini bertujuan untuk mengetahui hubungan pengetahuan ibu, motivasi ibu, \ndan dukungan bidan dengan inisiasi menyusui dini di Wilayah Kerja Puskesmas \nGajahan. Penelitian ini merupakan penelitian survei observasional dengan pendekatan \nCross sectional. Populasi penelitian adalah seluruh ibu yang melahirkan di wilayah \nKerja Puskesmas Gajahan pada bulan Juli 2014 sebanyak 74 ibu. Sampel sebanyak \n70 ibu yang diambil dengan Proporsional Random Sampling tiap kelurahan. Uji \nHipotesis menggunakan chi square test. Hasil penelitian menunjukkan bahwa ada \nhubungan antara pengetahuan ibu (p=0,000), motivasi ibu (p=0,001), dan dukungan \nbidan (p=0,009) dengan kesediaan ibu melakukan Inisiasi Menyusui Dini di Wilayah \nKerja Puskesmas Gajahan Kota Surakarta.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.046 | 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".