Child sexual abuse in southern Brazil and associated factors: a population-based study
Bibliographic record
Abstract
BACKGROUND: The prevalence of child sexual abuse (CSA) in the population has been poorly described in developing countries. Population data on child sexual abuse in Brazil is very limited. This paper aims to estimate lifetime prevalence of child sexual abuse and associated factors in a representative sample of the population aged 14 and over in a city of southern Brazil. METHODS: A two-stage sampling strategy was used and individuals were invited to respond to a confidential questionnaire in their households. CSA was defined as non-consensual oral-genital, genital-genital, genital-rectal, hand-genital, hand-rectal, or hand-breast contact/intercourse between ages 0 and 18. Associations between socio-demographic variables and CSA, before and after age 12, were estimated through multinomial regression. RESULTS: Complete data were available for 1936 respondents from 1040 households. Prevalence of CSA among girls (5.6% 95%CI [4.8;7.5]) was higher than among boys (1.6% 95%CI [0.9;2.6]). Boys experienced CSA at younger ages than girls and 60% of all reported CSA happened before age 12. Physical abuse was frequently associated with CSA at younger (OR 5.6 95%CI [2.5;12.3]) and older (OR 9.4 95%CI [4.5;18.7]) ages. CSA after age 12 was associated with an increased number of sexual partners in the last 2 months. CONCLUSION: Results suggest that CSA takes place at young ages and is associated with physical violence, making it more likely to have serious health and developmental consequences. Except for gender, no other socio-demographic characteristic identified high-risk sub-populations.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".