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Record W2564937027 · doi:10.1080/00224499.2016.1255874

Consequences of Casual Sex Relationships and Experiences on Adolescents’ Psychological Well-Being: A Prospective Study

2016· article· en· W2564937027 on OpenAlexafffundabout
Sophie Dubé, Francine Lavoie, Martin Blais, Martine Hébert

Bibliographic record

VenueThe Journal of Sex Research · 2016
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent Sexual and Reproductive Health
Canadian institutionsUniversité du Québec à MontréalUniversité Laval
FundersCanadian Institutes of Health Research
KeywordsCasualPsychologyDevelopmental psychologySocial psychologyClinical psychology

Abstract

fetched live from OpenAlex

Casual sexual relationships and experiences (CSREs) are still considered to be detrimental to the psychological well-being of youth even though findings remain inconclusive. Most studies have focused on emerging adulthood. Using a prospective design based on a representative sample of high school students in the province of Québec, we measured sexually active adolescents' (N = 2,304) psychological well-being six months after engaging in these relationships while controlling for prior well-being. We analyzed two forms of CSREs, friends with benefits (FWB) and one-night stand (ONS) relationships, as well as levels of sexual intimacy. The results show that CSREs had a small impact (small effect sizes) on subsequent psychological well-being, especially among girls; FWB relationships involving penetrative contact increased girls' psychological distress and both their alcohol and drug consumption. ONSs including sexual touching increased girls' psychological distress and their drug use. None of the CSREs influenced boys' psychological well-being. The findings underscored the importance of using caution when arguing that CSREs are detrimental or harmless to the psychological well-being of adolescents. The results also highlight the importance of taking into account gender and forms of CSREs in prevention and health interventions.

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)0.013
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.335
Threshold uncertainty score0.936

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.385
GPT teacher head0.558
Teacher spread0.173 · 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

Citations28
Published2016
Admission routes3
Has abstractyes

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