Online Sexual Exposure and Risky Behaviors: The Mediating Role of Sensation Seeking
Bibliographic record
Abstract
Objective: The objective of this study was to examine the relationship between online sexual exposure and risky sexual behaviors among Canadian adolescents and young adults, with sensation seeking tested as a mediating variable. Methods and Materials: A descriptive–correlational design was employed with a sample of 400 adolescents and young adults in Canada, selected based on Morgan and Krejcie’s sample size determination table. Participants completed validated self-report questionnaires measuring risky sexual behaviors, online sexual exposure, and sensation seeking. Descriptive statistics, Pearson correlation analyses using SPSS version 27, and Structural Equation Modeling (SEM) using AMOS version 21 were applied to test the hypothesized mediation model. Model fit was evaluated using chi-square, CFI, TLI, GFI, AGFI, and RMSEA indices. Findings: The results revealed that online sexual exposure was significantly and positively correlated with risky behaviors (r = .46, p < .001), while sensation seeking also showed a significant correlation with risky behaviors (r = .52, p < .001). SEM analysis demonstrated good model fit (χ² = 142.83, df = 71, χ²/df = 2.01, CFI = 0.96, TLI = 0.95, GFI = 0.94, RMSEA = 0.051). Online sexual exposure had a significant direct effect on risky behaviors (β = .29, p < .001) and an indirect effect through sensation seeking (β = .09, p = .002). The total effect of online sexual exposure on risky behaviors was β = .38 (p < .001), confirming partial mediation. Conclusion: These findings highlight that sensation seeking plays a crucial mediating role in linking online sexual exposure to risky sexual behaviors. Digital platforms not only increase exposure opportunities but also align with sensation-seeking tendencies, thereby heightening vulnerability to sexual risk.
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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".