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Record W4205432416 · doi:10.1080/00224499.2021.2011826

The Development of the Positive Sexuality in Adolescence Scale (PSAS)

2022· article· en· W4205432416 on OpenAlexaff
Chelly Maes, Emily A. Impett, Laura Vandenbosch

Bibliographic record

VenueThe Journal of Sex Research · 2022
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent Sexual and Reproductive Health
Canadian institutionsUniversity of Toronto
FundersBijzonder Onderzoeksfonds UGent
KeywordsHuman sexualityPsychologyDevelopmental psychologyConfirmatory factor analysisScale (ratio)Internal consistencyExploratory factor analysisConvergent validityExploratory researchClinical psychologyPsychometricsStructural equation modelingGender studies

Abstract

fetched live from OpenAlex

The aim of the present investigation was to develop a comprehensive tool to measure positive sexuality among adolescents. We first conducted an extensive literature review to develop the Positive Sexuality in Adolescence Scale (PSAS). We also conducted focus group interviews with adolescents (N = 14) to explore their understanding of positive sexuality and to discuss the proposed scale items. In two survey studies (Ntotal = 890), we examined the psychometric properties of the PSAS. In Study 1 (N = 211; Mage = 15.5, 55.5% girls), an exploratory factor analysis yielded five factors (e.g., positive approach to sexual relationships) which comprised 22 items. Convergent validity was also established in Study 1. In Study 2 (N =679; Mage = 15.32, 49% girls), a confirmatory factor analysis confirmed the factor structure. Results of Study 2 also supported the internal consistency and a partial measurement invariance for boys and girls. The PSAS is a useful tool for assessing the multifaceted nature of positive sexuality among adolescents for both boys and girls. We conclude by outlining future research directions on adolescent positivity sexuality using the PSAS.

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)0.035
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.235
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0350.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0050.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.004
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 head0.249
GPT teacher head0.533
Teacher spread0.284 · 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.

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

Citations16
Published2022
Admission routes1
Has abstractyes

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