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Record W4389737893 · doi:10.2196/50020

Latent Heterogeneity of Online Sexual Experiences and Associations With Sexual Risk Behaviors and Behavioral Health Outcomes in Chinese Young Adults: Cross-Sectional Study

2023· article· en· W4389737893 on OpenAlexvenueno aff
Ted C. T. Fong, Yee Tak Derek Cheung, Edmond Pui Hang Choi, Dyt Fong, Rth Ho, Patrick Ip, Man Chun Kung, M Lam, Antoinette M. Lee, William Chi Wai Wong, TH Lam, Paul Yip

Bibliographic record

VenueJMIR Public Health and Surveillance · 2023
Typearticle
Languageen
FieldPsychology
TopicSexuality, Behavior, and Technology
Canadian institutionsnot available
Fundersnot available
KeywordsLatent class modelMen who have sex with menPsychologyDemographySexual orientationHuman sexualityReproductive healthStructural equation modelingClinical psychologyGerontologyMedicineSocial psychologyPopulationSyphilis

Abstract

fetched live from OpenAlex

BACKGROUND: Online sexual experiences (OSEs) are becoming increasingly common in young adults, but existing papers have reported only on specific types of OSEs and have not shown the heterogeneous nature of the repertoire of OSEs. The use patterns of OSEs remain unclear, and the relationships of OSEs with sexual risk behaviors and behavioral health outcomes have not been evaluated. OBJECTIVE: This study aimed to examine the latent heterogeneity of OSEs in young adults and the associations with sexual risk behaviors and behavioral health outcomes. METHODS: The 2021 Youth Sexuality Study of the Hong Kong Family Planning Association phone interviewed a random sample of 1205 young adults in Hong Kong in 2022 (male sex: 613/1205, 50.9%; mean age 23.0 years, SD 2.86 years) on lifetime OSEs, demographic and family characteristics, Patient Health Questionnaire-4 (PHQ-4) scores, sex-related factors (sexual orientation, sex knowledge, and sexual risk behaviors), and behavioral health outcomes (sexually transmitted infections [STIs], drug use, and suicidal ideation) in the past year. Sample heterogeneity of OSEs was analyzed via latent class analysis with substantive checking of the class profiles. Structural equation modeling was used to examine the direct and indirect associations between the OSE class and behavioral health outcomes via sexual risk behaviors and PHQ-4 scores. RESULTS: The data supported 3 latent classes of OSEs with measurement invariance by sex. In this study, 33.1% (398/1205), 56.0% (675/1205), and 10.9% (132/1205) of the sample were in the abstinent class (minimal OSEs), normative class (occasional OSEs), and active class (substantive OSEs), respectively. Male participants showed a lower prevalence of the abstinent class (131/613, 21.4% versus 263/592, 44.4%) and a higher prevalence of the active class (104/613, 17.0% versus 28/592, 4.7%) than female participants. The normative class showed significantly higher sex knowledge than the other 2 classes. The active class was associated with male sex, nonheterosexual status, higher sex desire and PHQ-4 scores, and more sexual risk behaviors than the other 2 classes. Compared with the nonactive (abstinent and normative) classes, the active class was indirectly associated with higher rates of STIs (absolute difference in percentage points [Δ]=4.8%; P=.03) and drug use (Δ=7.6%; P=.001) via sexual risk behaviors, and with higher rates of suicidal ideation (Δ=2.5%; P=.007) via PHQ-4 scores. CONCLUSIONS: This study provided the first results on the 3 (abstinent, normative, and active) latent classes of OSEs with distinct profiles in OSEs, demographic and family characteristics, PHQ-4 scores, sex-related factors, and behavioral health outcomes. The active class showed indirect associations with higher rates of STIs and drug use via sexual risk behaviors and higher rates of suicidal ideation via PHQ-4 scores than the other 2 classes. These results have implications for the formulation and evaluation of targeted interventions to help young adults.

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)0.000
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 score0.083
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.065
GPT teacher head0.433
Teacher spread0.368 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

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