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Record W2979147308 · doi:10.36419/jkebin.v10i2.282

EVALUASI PENCATATAN KOHORT BAYI DI WILAYAH KABUPATEN PEKALONGAN

2019· article· id· W2979147308 on OpenAlexaff
Rini Kristiyanti, Pujiati Setyaningsih, Nuniek Nizmah Fajriyah

Bibliographic record

VenueJurnal Kebidanan Indonesia · 2019
Typearticle
Languageid
FieldMedicine
TopicPublic Health and Nutrition
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsPsychology

Abstract

fetched live from OpenAlex

Latar belakang: Keberhasilan upaya pelayanan kesehatan pada bayi dapat diketahui melalui cakupan pelayanan kesehatan bayi, yang didalamnya menggambarkan kinerja bidan dalam memberikan pelayanan kepada bayi. Kohort bayi merupakan salah satu instrumen kesehatan ibu dan anak yang merupakan sumber data tentang bayi di suatu wilayah kerja bidan. Evaluasi pencatatan kohort perlu dilakukan agar dapat mengetahui sejauh mana instrumen tersebut bermanfaat dan untuk menentukan program ke depan berkaitan dengan kesehatan ibu dan anak.
 Tujuan: Menggambarkan evaluasi pencatatan kohort bayi di wilayah Kabupaten Pekalongan.
 Metode: Penelitian ini merupakan penelitian deskriptif dengan pendekatan cross sectional. Populasi penelitian ini adalah seluruh bidan di wilayah kabupaten Pekalongan sejumlah 339 orang. Teknik pengambilan sampel dengan menggunakan purposive sampling didapatkan 6 puskesmas dengan subyek penelitian sejumlah 57 orang. Pada penelitian ini menggunakan checklist yang diisi sesuai dengan hasil kohort bayi masing-masing subyek penelitian. Analisa data menggunakan analisis univariat.
 Hasil: Item dalam kohort bayi diisi dengan lengkap sebesar 16,7% (No.urut, nama bayi, jenis kelamin, jenis kelamin, alamat, dan kondisi saat lahir), sedangkan item yang lain diisi tidak lengkap. Hasil tidak lengkap paling sering ditemui pada item NIK (93%), kematian (98,2%), masuk balita (75,4%), dan keterangan (82,5%)
 Simpulan: Sebagian besar (83,3%) item dalam kohort bayi belum diisi dengan lengkap oleh bidan

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.006

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.

Opus teacher head0.017
GPT teacher head0.290
Teacher spread0.274 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations1
Published2019
Admission routes1
Has abstractyes

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