Interventions that Foster Healing Among Sexually Exploited Children and Adolescents: A Systematic Review
Bibliographic record
Abstract
Sexual exploitation of children and adolescents is a pressing, yet largely under-recognized form of child abuse. The goals of this review were to identify interventions that have been implemented with sexually exploited children and adolescents and to better understand their effectiveness for fostering healing with this population. Our systematic search generated 4,358 publications of which 21 met our inclusion criteria. Based on their objectives and delivery, we organized the interventions into five categories: (a) focused health and/or social services, (b) intensive case management models, (c) psychoeducational therapy groups, (d) residential programs, and (e) other. Most programs were gender-specific, targeting girls and young women with just one being for boys and young men only. Studies reported on a range of outcomes including psychosocial outcomes, risky behaviors, trauma responses, mental health, protective factors, and public health outcomes. Despite differences in delivery, most of the interventions did, to some degree, appear to foster healing among sexually exploited children and adolescents. Findings from this review have implications for researchers, policy and program developers, and frontline practitioners who, through partnerships with one another, can advocate for the creation of evidence-informed, purpose-built, and thoughtfully delivered interventions.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".