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Record W2418796662

Substance use and sexual risk behaviors among Mississippi public high school students.

2012· article· en· W2418796662 on OpenAlexaboutno aff
James G. McGuire, Bo Wang, Lei Zhang

Bibliographic record

VenuePubMed · 2012
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent Sexual and Reproductive Health
Canadian institutionsnot available
Fundersnot available
KeywordsSexual intercourseYouth Risk Behavior SurveyCondomPsychological interventionPsychologyQuarter (Canadian coin)DemographyLogistic regressionMedicineBinge drinkingClinical psychologyPsychiatryEnvironmental healthPoison controlHuman immunodeficiency virus (HIV)Suicide preventionPopulationFamily medicine
DOInot available

Abstract

fetched live from OpenAlex

This study describes the patterns of substance use and sexual risk behaviors and examines the relationships among a representative sample of Mississippi public high school students. Data were obtained from the 2009 Mississippi Youth Risk Behavior Survey. Multiple logistic regression analyses were performed. We found 61% of the participants ever had sexual intercourse and 13.4% engaged in early sexual initiation (< or = 12 years). Nearly a quarter had four or more lifetime sexual partners. One-third did not use a condom during their last sexual intercourse. Two-thirds drank alcohol. Over one-third used marijuana. Older age, being a black, drinking alcohol, or using marijuana or other drugs were associated with early sexual initiation and having multiple sexual partners. Heavy smoking was associated with early sexual initiation. Using marijuana or other drugs was associated with unprotected sex. Findings highlight the extensive substance use and engagement of sexual risk behaviors among Mississippi adolescents. Interventions that address both substance use and sexual risk behaviors may have a great impact in preventing teen pregnancy and HIV/STD transmission and curtailing substance abuse problems among Mississippi adolescents.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.013
Threshold uncertainty score0.861

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.157
GPT teacher head0.390
Teacher spread0.232 · 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.

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".

Quick stats

Citations10
Published2012
Admission routes1
Has abstractyes

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