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Record W4393354754 · doi:10.36419/avicenna.v7i1.1030

PERBEDAAN PENGETAHUAN IBU HAMIL TENTANG TRIPEL ELIMINASI SEBELUM DAN SESUDAH PEMBERIAN EDUKASI DENGAN BOOKLET

2024· article· id· W4393354754 on OpenAlexaff
Desi Wijayanti Eko Dewi, Shinta Ika Sandhi

Bibliographic record

VenueAvicenna Journal of Health Research · 2024
Typearticle
Languageid
FieldMedicine
TopicPublic Health and Nutrition
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsMedicine

Abstract

fetched live from OpenAlex

Latar belakang : Eliminasi penularan HIV, sifilis dan hepatitis B dari ibu ke anak dalam program tripel eliminasi perlu dilakukan penanggulangan yang terintegrasi, komprehensif berkesinambungan, efektif dan efisien dengan indikator berupa infeksi baru HIV, sifilis dan atau hepatitis B pada anak kurang dari atau sama dengan 50/100.000 kelahiran hidup. Pelaksanaan tripel eliminasi dilakukan melalui promosi kesehatan, surveilans kesehatan, deteksi dini dan atau penanganan kasus. Pengetahuan ibu hamil dapat ditingkatkan dengan melakukan edukasi menggunakan media booklet yang mengutamakan pesan-pesan visual dalam bentuk buku baik berupa tulisan atau gambar.Tujuan : Menganalisis perbedaan pengetahuan ibu hamil tentang tripel eliminasi sebelum dan sesudah pemberian edukasi dengan booklet.Metode : Jenis penelitian adalah pre eksperimen, dengan desain one group pre test and post test design menggunakan pendekatan cross sectional. Pengumpulan data secara observasi menggunakan kuesioner pada 33 responden. Analisa data yang digunakan secara univariat dan bivariat menggunakan uji pair "t" test Wilcoxon.Hasil : Pengetahuan ibu hamil sebelum pemberian edukasi didapatkan nilai mean 52,67, dengan standar deviasi 15,753, sedangkan pengetahuan ibu hamil sesudah pemberian edukasi didapatkan nilai mean 91,30dengan standar deviasi 11,698.Simpulan : Terdapat perbedaan pengetahuan ibu hamil tentang tripel eliminasi yang bermakna antara sebelum dan sesudah pemberian edukasi dengan booklet.

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)0.045
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.571
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0450.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.003
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.009
Insufficient payload (model declined to judge)0.0010.001

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 head0.207
GPT teacher head0.487
Teacher spread0.280 · 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; a candidate call from one teacher head, not a consensus.

Study designNot applicable
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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Citations0
Published2024
Admission routes1
Has abstractyes

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