Cross-Sectional and Longitudinal Associations Between Pornography Use and Dating Violence Victimization: Are There Risks for Teenagers?
Bibliographic record
Abstract
Dating violence victimization (DVV) is a prevalent public health problem with harmful consequences among adolescents. Pornography use has been identified among the factors associated with DVV. However, most studies have relied on cross-sectional designs, limiting the ability to determine temporal relationships between these variables. The present study assessed bidirectional longitudinal associations between pornography use and DVV (psychological, physical, and sexual), also examining cross-sectional associations and gender differences. Participants’ self-report data from two assessments of a longitudinal study were used. The sample consisted of 1,556 teenagers ( M age = 14.55 years, SD age = .630; 51.5% were girls) having reported an intimate relationship in the past year at the first and/or second time point (T1/T2). Whereas some cross-sectional associations between pornography use and DVV were observed at T1, results from the autoregressive cross-lagged model revealed no significant longitudinal association between pornography use and the three forms of DVV, regardless of gender. Thus, pornography use may not represent a significant risk factor over time for DVV in adolescents. These findings provide additional insights concerning the associations between pornography use and DVV and suggest that emphasis should perhaps be placed on other variables in the study of risk factors for DVV. Still, although modest, transversal links support the importance of interventions that promote healthy intimate relationships in adolescence and education about pornography use.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".