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Record W4376999732 · doi:10.33087/jiubj.v23i1.3210

Faktor-Faktor yang Berhubungan dengan Perilaku Seks Pranikah pada Remaja di SMA Negeri 2 Sarolangun Tahun 2022

2023· article· en· W4376999732 on OpenAlexaff
Dewi Reknowati, Desy Susanti

Bibliographic record

VenueJurnal Ilmiah Universitas Batanghari Jambi · 2023
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent Health and Behaviors
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsCuriosityPremarital sexSMA*PsychologyPopulationSexual intercourseBivariate analysisMarital statusCondomDemographySexual behaviorDevelopmental psychologySocial psychologyMedicineSociologyMathematicsStatistics

Abstract

fetched live from OpenAlex

According to IDHS (2012), the prevalence of pre-marital sex cases was 14.6% among young women aged 15-19 years. The reasons for premarital sexual intercourse were mostly due to curiosity/curiosity (57.5%), it just happened to women (38%), and was forced by partners (12.6%) to women. The aim of the research is to find out the factors related to premarital sexual behavior in adolescents at SMA Negeri 2 Sarolangun in 2022. This research is analytic in nature with a cross sectional research design. The research was carried out at SMA Negeri 2 Sarolangun and was carried out in June 2022. The population for this research was all students of class X and XI SMA Negeri 2 Sarolangun as many as 243 people. The sample of this research is 70 people. The data analysis used was univariate and bivariate analysis. The results showed that there was a relationship between family support and premarital sex behavior in adolescents with a p-value = 0.000, there was a relationship between information sources and premarital sex behavior in adolescents with a p-value of 0.003, and there was a relationship between knowledge and premarital sex behavior in adolescents. adolescents with a p-value of 0.001.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, 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 score0.054
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0030.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.003

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 head0.041
GPT teacher head0.362
Teacher spread0.321 · 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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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