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Record W4323649350 · doi:10.55919/jk.v5i3.46

KARAKTERISTIK IBU YANG MENGGUNAKAN KONTRASEPSI IMPLANT DI WILAYAH KERJA PUSKESMAS YOSODADI TAHUN 2018

2021· article· id· W4323649350 on OpenAlexaff
Admin Admin, Tri Susanti

Bibliographic record

VenueJurnal Kesehatan · 2021
Typearticle
Languageid
FieldMedicine
TopicPublic Health and Nutrition
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsMedicineGynecology

Abstract

fetched live from OpenAlex

Berdasarkan data prasurvei di Wilayah Kerja Puskesmas Yosodadi Metro Timur Tahun2017, Jumlah PUS sebanyak 2197, dan Jumlah pengguna kotrasepsi aktif sebanyak 1613,dengan pengguna KB Suntik sebanyak 755 ( 34,4 %), KB Pil sebanyak 326 ( 14,8 %), KBImpalnt sebanyak 228 ( 10,4 %), KB IUD sebanyak 192 ( 8,7 %), KB MOW sebanyak 73 (3,32 %), Kondom sebanyak 32 ( 1,4 %), MOP sebanyak 7 ( 0,31 %). Tujuan dari penelitianuntuk mengetahui karakteristik ibu yang menggunakan kontrasepsi implant. Subyaekpenelitian yaitu ibu kaseptor KB Implant sedangkan objek penelitian adalah karakteristik ibu.Metode penelitian yang di gunakan yaitu metode deskriptif. Populasinya adalah seluruh ibuyang menggunakan kontrasepsi implant di wilayah kerja puskesmas Yosodadi tahun 2018yang berjumlah 268 akseptor. sampel yang di ambil dengan tehnik total sampling. Cara ukuryang di gunakan dengan alat ukur berupa rekam medik dianalisis secara univariat dengandistribusi frekuensi. Hasil penelitian Karakteristik ibu yang menggunakan kontrasepsi implantdi Wilayah Kerja Puskesmas Yosodadi Tahun 2015 bahwa distribusi frekuensi distribusifrekuensi umur ibu sebagian besar dengan umur 20-35 tahun sebanyak 138 ibu (51,5%),paritas multipara sebanyak 142 ibu (53,0%), pendidikan menengah sebanyak 115 ibu (43,0%).Kesimpulannya adalah karakteristik ibu yang menggunakan kontrasepsi Implant adalah umur20-35 tahun. Paritas multipara, pendidikan menengah. Disarankan kepada responden untukmenambah pengetahuan dan pemahaman mereka tentang manfaat dan tujuan Kontrasepsi.

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.273
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.024
GPT teacher head0.296
Teacher spread0.272 · 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".

Quick stats

Citations2
Published2021
Admission routes1
Has abstractyes

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