Prevalence and Correlates of Sexting Behaviors in a Provincially Representative Sample of Adolescents
Bibliographic record
Abstract
OBJECTIVES: To examine the prevalence and correlates of sending and receiving sexts (i.e., sexually explicit images) in a provincially representative sample of adolescents in Canada. METHODS: Data from the 2014 Ontario Child Health Study, a provincial survey of households with children in Ontario, which includes a sample of 2,537 adolescents aged 14 to 17 years (mean age = 15.42, male = 51.6%) were used to address the research objectives. RESULTS: The past 12 months prevalence of sending and receiving sexts was 14.4% and 27.0%, respectively. In unadjusted logistic regression analyses, non-White adolescents and those living in low-income households were less likely to send or receive sexts compared to White and non-low-income adolescents. Adolescents who disclosed their sexual and/or gender minority identities were 3 to 4 times more likely to send and receive sexts than youth who had not disclosed these identities. Higher levels of mental health problems generally observed among adolescents who sent or received sexts. In fully adjusted models, low income and ethnic minority status were associated with reduced odds of sending and receiving sexts, while sexual and/or gender minority disclosure status was associated with increased odds. Social anxiety was associated with reduced odds of sending and receiving sexts, while conduct disorder was associated with elevated odds. CONCLUSION: The prevalence of sexting behavior was higher among adolescents who disclosed their sexual or gender minority identities. Sexting behaviors were associated with higher levels of mental health problems. Identifying vulnerable populations and the potential mental health ramifications associated with sexting behavior is vital to mitigating negative sequelae.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".