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Record W4312356956 · doi:10.31983/juk.v2i1.8786

FAKTOR RISIKO POLA SEKSUALITAS PADA WANITA LESI SERVIK

2022· article· ms· W4312356956 on OpenAlexaff
Bambang Sarwono, Pramono Giri Kriswoyo

Bibliographic record

VenueJuru Rawat Jurnal Update Keperawatan · 2022
Typearticle
Languagems
FieldSocial Sciences
TopicLegal and Policy Analysis in Indonesia
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsMedicineGynecology

Abstract

fetched live from OpenAlex

Latar belakang. Insiden lesi serviks yang dapat berlanjut menjadi kanker serviks diperkirakan mencapai 100 per 100.000 penduduk. Pada tahun 2013, kanker serviks merupakan penyakit kanker terbanyak di Indonesia (0,8%). Kejadian kanker serviks di Kab Magelang pada tahun 2018 mencapai 2.3%, lebih tinggi daripada kejadian di Provinsi Jawa Tengah. Penelitian ini bertujuan untuk mengetahui  besarnya faktor risiko kejadian lesi serviks karena pola seksualitas di wilayah Kabupaten Magelang.Tujuan dalam penelitian ini adalah untuk mengetahui beberapa faktor risiko kejadian lesi servik serta mengetahui faktor apa yang paling berpengaruh pada kejadian tersebut di Kab Magelang tahun 2020. Dengan mengetahu faktor-faktor risiko masyarakat tahu untuk mengatisipasinyaMetode Penelitian. Penelitian ini menggunakan Survey analitik. Populasi penelitian ini adalah seluruh wanita yng memiliki pasangan di usia subur. Sampel diambil menggunkan Accidental sampling yaitu  pasien yang melakukan pemeriksaan IVA di bidan praktik mandiri di wilayah Kab Magelang.Hasil penelitian faktor yang tidak berpengaruh terhadap kejadian lesi servik antara lain umur responden, pengalaman pertama hubungan sek (p 0,548), metode KB (p 0,451) dan hygiene alat kelamin (p 0,512) Adapun faktor yang berkontribusi kejadian lesi servik adalah jumlah pasangan seksual (p 0,164, OR 0,378), penggunaan alat bantu (p 0,000, OR 8,634) dan frekuensi sek (p 0,000, OR 2,888)Kata Kunci : Seksual, Lesi Servik

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.329
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0080.001
Scholarly communication0.0010.001
Open science0.0040.002
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0150.002

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 head0.021
GPT teacher head0.304
Teacher spread0.282 · 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 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
Published2022
Admission routes1
Has abstractyes

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