Analysis of reported cases of sexual violence in Espírito Santo, southeastern Brazil, 2011–2018
Bibliographic record
Abstract
BACKGROUND: sexual violence includes all sexual acts consummated or attempt to obtain them, unwanted sexual comments and actions that go against the other's sexuality through coercion, which can be done through the use of physical force, psychological pressure, extortion or threat, this phenomenon appears in all life cycles. Identified the frequency and characteristics of sexual violence against women in a state in the southeastern region of Brazil. from 2011 to 2018. METHOD: this is a cross-sectional epidemiological study that evaluated all cases of sexual violence reported in Espírito Santo, present in the Information System of Diseases and Notifications of the Ministry of Health from 2011 to 2018. The analysis was based on performed in Stata 14.1. RESULTS: the prevalence of notification of sexual violence was 13.2% (CI95%: 12.8-13.5). Most victims were women (PR: 3.38), aged between 0 and 9 years (PR: 19), with a higher prevalence in people without disabilities or disorders (PR: 1.18) and residents of urban/periurban area (PR: 1.15). Men were the most frequent aggressors (PR: 13.79), with the most prevalent cases being reported by people unknown to the victim (PR: 6.01). The occurrence was 78% more reported at home and committed by an aggressor (PR:1.19). Most cases were repeated (PR:1.13). CONCLUSIONS: the notification of sexual violence in Espírito Santo was high and evidenced the vulnerability of some groups, as well as the profile of the perpetrators. It is necessary to work on training professionals in the areas of health and education in relation to the identification of cases of sexual violence, mainly due to the significant involvement of children and adolescents.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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".