Associations between receiving non-consensual image and video sexts and average sleep duration among adolescents and young adults
Bibliographic record
Abstract
Background Sexting is the sending and receiving of nude or partially nude images or videos. Despite it being a part of contemporary relationships, it can have adverse effects. This is particularly the case when receiving non-consensual sexts. To date, there remains a gap in the literature on whether receiving non-consensual sexts is associated with poor sleep. Therefore, the aim of this study was to determine the association between receiving non-consensual sexts and average sleep duration. Methods Data from Wave 2 (2022) of the Canadian Study of Adolescent Health Behaviours (N =906) were analysed. Multinomial logistic regression analyses were used to determine the association between receiving non-consensual sexts (both image and video) in the past 12months and average sleep duration (≤5h, 6h, 7h, 8h, and ≥9h) in the past 2weeks. Analyses were stratified by gender. Results Girls and women who received non-consensual image and video sexts, compared to those who did not, were more likely to report ≤5h of average sleep in the past 2weeks, relative to 8h of average sleep. There were no significant findings among boys and men. Conclusion Findings underscore that receiving non-consensual image and video sexts may negatively impact sleep among girls and women, which may be contextualised by trauma responses experienced because of gender-based sexual violence. Healthcare and mental health professionals should be made aware of this association to provide effective care to girls and women.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".