Sexual Violence against Adolescents in the State of Espírito Santo, Brazil: An Analysis of Reported Cases
Bibliographic record
Abstract
Objective: We describe the prevalence of the reported cases of sexual violence against adolescents and analyze their associated factors. Methods: A cross-sectional analytical study (n = 561) was conducted with reported data on sexual violence against adolescents in the state of Espírito Santo registered in SINAN between 2011 and 2018 to understand the prevalence and predictors of sexual violence against adolescent victims, as well as to describe the perpetrators and the nature of the aggression. Variables to characterize the victim, aggression, and perpetrator were used. Bivariate analyses were performed using chi-square (χ2) and Fisher’s exact tests, and multivariate analyses were conducted using log-binomial models; the results were presented with prevalence ratios. All analyses were stratified by sex. Results: The prevalence of sexual violence was 32.6%, and 93% of the victims were female. In both males and females, the reported sexual violence was associated with a younger age (10–12 years old), living at home, being related to the perpetrator, and a history of sexual violence. In females, the reported sexual violence was also associated with the number of perpetrators, and in males, with the perpetrator’s age. Conclusions: Our findings show the high frequency of reporting of sexual violence and the characteristics of the victim, the aggression, and the aggressor as factors associated with its occurrence in both sexes. The importance of health information systems for disseminating data and the need for measures to prevent and treat the violence among adolescents is urgent.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".