Bibliographic record
Abstract
Child sexual abuse is a common and devastating problem affecting as many as 15 to 30% of girls. Perpetrators of child sexual abuse are more likely to be male; most often someone known to the child. It is now well established that child sexual abuse is a non-specific risk factor for both internalizing and externalizing disorders in girls and adult women, and is associated with neurobiological dysregulation in both childhood and adulthood. Children’s exposure to sexual abuse continues to be underrecognized and underdetected. Generally, sexual abuse of a child is detected when a child discloses to another person. A comprehensive assessment is the first step in determining the treatment needs for a child who has been sexually abused and should include evaluation of risk for recurrence, as well as the child’s behavioral, emotional and cognitive functioning and the family environment, including level of support. Cognitive-behavioral therapy for sexually abused children with symptoms of posttraumatic stress disorder (PTSD) shows the best evidence for reducing subsequent impairment; however, it is important to consider the child’s context and risk of recurrence when determining treatment needs. Although the main focus of sexual abuse prevention has been on education programs aimed at children, and offender management, it remains unknown whether such programs actually prevent child sexual abuse. Most information about sexual abuse of girls is based on studies from high-income countries; further research is needed to improve our understanding of child sexual abuse experienced by children in low and middle-income countries and global strategies for prevention.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.004 |
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".