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Record W3007718356 · doi:10.34305/jikbh.v10i2.101

STUDI DESKRIPTIF TINGKAT PENGETAHUAN IBU TENTANG ASCARIASIS (CACINGAN) PADA BALITA DI WILAYAH KERJA PUSKESMAS SIWULUH KABUPATEN BREBES TAHUN 2019

2019· article· id· W3007718356 on OpenAlexaff
Rosmalia Kamil

Bibliographic record

VenueJurnal Ilmu Kesehatan Bhakti Husada Health Sciences Journal · 2019
Typearticle
Languageid
FieldMedicine
TopicPublic Health and Nutrition
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsMedicineGynecologyHumanitiesArt

Abstract

fetched live from OpenAlex

Cacingan merupakan kondisi di dalam tubuh manusia terdapat cacing. Cacingan dapat mengakibatkan menurunnya kondisi kesehatan, gizi, kecerdasan, dan produktivitas penderitanya sehingga secara ekonomi banyak menyebabkan kerugian. Cacingan bisa terjadi pada orang dewasa namun lebih banyak kejadian terjadi pada usia anak-anak. Hal ini dapat disebabkan anak yang kurang menjaga kebersihan diri terutama pada saat mereka sedang bermain. Penelitian ini bertujuan untuk mengetahui pengetahuan ibu tentang gambaran tingkat pengetahuan ibu tentang ascariasis (cacingan) pada balita di Wilayah Kerja Puskesmas Siwuluh Kabupaten Brebes. Metode penelitian yang digunakan adalah deskriptif dengan metode survey yang bertujuan untuk mengetahui gambaran pengetahuan ibu tentang cacingan pada balita di Wilayah Puskesmas Siwuluh. Pengambilan data menggunakan kuesioner. Berdasarkan hasil penelitian 50 responden ibu yang mempunyai balita, menunjukkan pengetahuan responden dikategorikan berpengetahuan baik sebanyak 9 responden (18%), Cukup sebanyak 13 responden (26%), dan kurang 28 responden (56%). Dapat disimpulkan bahwa sebagian besar ibu di Wilayah Kerja Puskesmas Siwuluh berpengetahuan kurang. Hasil penelitian ini dapat dijadikan acuan untuk data awal

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.037
GPT teacher head0.337
Teacher spread0.301 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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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Citations13
Published2019
Admission routes1
Has abstractyes

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