Patterns of sexting and sexual behaviors in youth: A Latent Class Analysis
Bibliographic record
Abstract
INTRODUCTION: A sizable minority of youth are sexting; however there are likely large individual differences in sexting and sexual behaviors, yet to be captured. A Latent Class Analysis was used to identify subgroups of youth characterized by differential engagement in sexting and sexual behaviors. METHODS: Participants were an ethnically diverse sample of 894 youth (55.8% female; Mage = 17.04, SD = 0.77) from a longitudinal survey study in southeast Texas. Latent classes were identified through participants' responses to the following indicator variables: sending, receiving, and requesting sexts, sexual activity, contraception use, ≥ three partners, and substance use prior to sexual activity. Gender, ethnicity, impulsivity, and living situation were analyzed as predictors, and depressive symptoms as an outcome, of class membership. RESULTS: The analysis revealed four distinct classes: No sexting-Low sex (42.2%), Sexting-Low sex (4.5%), No sexting-Moderately risky sex (28.3%), and Sexting-Moderately risky sex (24.9%). Gender and ethnicity predicted class membership wherein females and ethnic minority youth were less likely to be in groups displaying higher rates of sexting. Impulsivity and living situation predicted class membership, such that youth reporting higher impulsivity and living in a situation other than with two biological parents were less likely to be in classes displaying low sexting and sexual behaviors. Group membership predicted depressive symptoms. CONCLUSIONS: Results suggest that not all youth who are sexting are having sex, and not all youth who are having sex are sexting. Evidence of individual differences in youth sexual behaviors should inform educational initiatives aimed at teaching youth about sexual and online health.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".