Differential Profiles of Sexually Abused Adolescent Boys
Bibliographic record
Abstract
Victims of child sexual abuse (CSA) are a heterogeneous population. Several characteristics may influence the outcomes associated with this adverse childhood experience, including personal (e.g. age) and CSA characteristics (e.g. relationship to the perpetrator). This study relied on a person-centered approach to account for this heterogeneity and focused on adolescent boys, an understudied population. Data were drawn from a representative sample of high school students aged 14 to 18 years old in Quebec, Canada. A total of 3.9% (n = 138) of boys reported CSA. Various CSA characteristics (severity, relationship to the perpetrator, and number of events) were used as indicators to derive classes. A four-class solution emerged from the latent class analysis: CSA in a sports context (6%), intrafamilial CSA (8%), extrafamilial CSA (52%) and multiple CSA (34%). The multiple CSA profile included boys who were sexually abused in multiple situations by different perpetrators and who were victims of acts involving penetration. The exploration of correlates associated with class membership revealed that adolescent boys included in the multiple CSA profile were distinguished by higher rates of delinquent behaviors and alcohol and drug use. They were more likely than members of other latent classes to belong to sexual minorities. This exploratory study sheds light on sexually victimized adolescent boys and the deleterious consequences that may affect them, particularly boys who have experienced multiple CSA events. We conclude that prevention efforts should focus on the demystification of sexual trauma among boys and on using trauma-informed care approaches for adolescent externalizing behaviors.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".