Adapting Adolescent Dating Violence Prevention Interventions to Victims of Child Sexual Abuse
Bibliographic record
Abstract
Considering the increased risk of revictimization, adolescents who have experienced child sexual abuse (CSA) are a priority subpopulation for the prevention of dating violence. Yet, intervention programs often focus on psychological symptomology associated with CSA; few tackle issues specific to relational violence. Addressing the relational traumatization of adolescents with a history of CSA is essential to prevent their revictimization. Given specific CSA sequelae related to intimacy and engagement in sexual behaviors, there is a need for tailoring interventions to boy and girl survivors. A case study of a group intervention designed for adolescent girls with a history of CSA was conducted. The context adaptation, based on intervention mapping proposed by Bartholomew and colleagues, served as a theoretical framework. Four steps were taken to ensure that the intervention addressed CSA youth needs: (a) needs assessment, (b) analysis of the conceptual framework of the original program, (c) selection of interventions and developing new interventions, and (d) validation with a committee of practitioners. This approach provided an understanding of risk factors and intervention priorities using the problem logic model. The original program was enhanced by adding four interventions addressing the prevention of dating violence. These interventions were then validated by practitioners before implementation in the setting. The approach underscores the relevance of understanding the needs of the clientele and of adopting a collaborative approach to ensure the proposed interventions are relevant.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".