When Victims Become Abusers: A Study Among the Male Victims of Child Sexual Abuse in Bangladesh
Bibliographic record
Abstract
There are substantial mental health consequences for male child sexual abuse (MCSA) victims. Survivors may exhibit sexually offensive actions because of this trauma. In other words, the abused becomes an abuser. In Bangladesh, MCSA is an invisible social problem. This study aimed to assess sexually offensive behaviors among victims of MCSA and to determine the associated factors. A total of 540 victims participated in an online survey as part of a cross-sectional study. Data were collected on victimization, suicidal ideation, history of offense, and sociodemographic factors. Pearson chi-square test and a binary logistic regression were employed to assess significant factors. Results revealed that 63.2% of participants reported engaging in sexually offensive behavior. Those living in villages, unmarried, experienced repetitive sexual abuse, under 13 years old at the time of abuse, experienced physical abuse concurrently, being penetrated during abuse, not disclosing the abuse, not receiving psychological assistance, having significant sexual involvement with men, and experiencing suicidal ideation were more likely to exhibit sexually offensive behaviors. The study underscores the importance of policymakers implementing relevant policies to safeguard boys. In addition, it emphasizes the need for victims to disclose instances of sexual abuse and actively seek psychological intervention.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".