A Multidisciplinary Scoping Review of Interventions to Support Victims of Human Trafficking
Bibliographic record
Abstract
Victims of human trafficking experience extensive trauma, including multiple types of abuse, malnutrition, and unsafe living and working conditions. Supporting victims is complex and requires a multi-sectoral response which includes child welfare, criminal justice, public health, mental health, and social services. The purpose of this scoping review was to identify and assess multi-sectoral interventions to support victims of human trafficking. We searched Medline, Embase, CINAHL, PsycINFO, Social Services Abstracts and Social Work Abstracts. All screening and data collection was completed by two independent reviewers. Conflicts were resolved via consensus with the research team. We identified a total of 17,309 papers of which 43 were included for data extraction. In these 43 studies, we identified seven key types of interventions: psychotherapies, housing interventions, residential treatment facilities, psychosocial interventions, case management, occupational therapy, and legal interventions. Of these interventions, only 37% were multi-sectoral in nature. Our findings highlight the need for large-scale, randomized control trials of multi-component interventions to meet the diverse needs of this population. The use of a multiphase optimization strategy (MOST) approach may be especially beneficial here as it allows for the optimization of individual intervention components before a formal randomized controlled trial (RCT) evaluation.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".