Bibliographic record
Abstract
BACKGROUND: Previous studies have tried to determine the relationship between sexting and risky behaviour to discover whether sexting fits into a deviance or normalcy discourse. This study investigated the relationship between sexting and sexual risk behaviours, contraception use and gender. METHODS: The design was a cross-sectional analysis of data from the sixth National Survey of Secondary Student and Adolescent Sexual Health, collected in 2018. There were 8263 Australian adolescents (aged 14-18years). Participants were fairly evenly split by gender, and 73% identified as heterosexual. Participants were asked a series of questions about their engagement in sexting, sexual behaviour and sexual health behaviours. RESULTS: A total of 52% of participants had sent a sext in the previous 2months, with most being text-based sexts. Sexters were 3.29times more likely to have engaged in anal or vaginal intercourse, and 2.88times more likely to have gotten pregnant than non-sexters. Sexters (M =2.76) had significantly more partners than non-sexters (M =2.35), t (3763)=-10.99, P X 2 (1)=0.38, P =0.535, or contraceptive use based on sexting status. CONCLUSIONS: Sexters are more likely to have engaged in sexual intercourse and have more partners than non-sexters. Sexting is not strongly associated with other risky behaviours. Evidence for differences between sexters and non-sexters in protecting against STIs and pregnancy was not found, as there were no significant differences in contraceptive use.
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 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 | 0.002 | 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 | 0.001 | 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".