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Record W4402567903 · doi:10.1080/15546128.2024.2388226

An Exploration of Sexual Consent Beliefs, Attitudes, and Behaviors among Middle School Students in Canada

2024· article· en· W4402567903 on OpenAlexafffundabout
Carolyn O’Connor, Ramona Alaggia, Stephanie Begun, Zoë D. Peterson

Bibliographic record

VenueAmerican Journal of Sexuality Education · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicSexual Assault and Victimization Studies
Canadian institutionsUniversity of Toronto
FundersPublic Health Agency of Canada
KeywordsPsychologyHuman sexualityDevelopmental psychologySexual orientationSexual behaviorSexuality educationSocial psychologySex educationGender studiesSociology

Abstract

fetched live from OpenAlex

An essential component of healthy sexual exploration is the understanding of sexual consent. Unfortunately, sexual consent is not consistently taught in sex education programs developed for adolescents. Furthermore, although early adolescence is a critical developmental period for the formation of sexual identity, norms, attitudes, and relationships, this population is underrepresented in the sexual consent literature. To fill this important gap, this study examined the sexual consent beliefs, attitudes, and behaviors of 254 middle school students from a large, urban city in the province of Ontario. Correlates and predictors of sexual consent endorsement were tested via bivariate and multiple regression analyses. Greater perceived negative sanctions of dating violence and higher endorsement of gender stereotypes emerged as variables significantly related to and predictive of sexual consent attitudes, perceived behavioral control, and affirmative consent norms. Gender was also a significant correlate, as girls were more likely than boys to endorse affirmative sexual consent beliefs, attitudes, and behaviors. The findings highlight important group differences and provide guidance for the design and implementation of sexual education and prevention programs.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.346
Threshold uncertainty score0.784

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.061
GPT teacher head0.410
Teacher spread0.350 · 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

Citations1
Published2024
Admission routes3
Has abstractyes

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