Human Trafficking Detection in Health Care Settings: A Scoping Review
Bibliographic record
Abstract
Abstract Objectives This scoping review aimed to identify available tools for detecting human trafficking survivors in healthcare settings and to explore implementation strategies. Methods A systematic search was conducted in MEDLINE/PubMed, EMBASE, BIREME-LILACS, WHO-PAHO IRIS, and other institutional repositories on August 12, 2024, with an update on January 18, 2025. Results Out of 2,881 records screened, 140 resources were included, mostly from the U.S. (n = 113). We identified 26 screening tools, 44 toolkits and guidelines, 23 documents on educational strategies and 24 studies on other types of strategies targeting HT detection in healthcare. Tools often addressed sexual exploitation, especially in minors, with limited focus on other forms of trafficking. Only eight tools reported having undergone validation processes. Many resources emphasized trauma-informed care, indicator use, and referral protocols. Implementation strategies included training programs, integration of screening protocols into clinical workflows, and digital tools; however, system-level barriers and limited provider confidence persist. Conclusion Despite the availability of tools and guidelines, there is no consensus on definitions or standardized methods for HT identification. Most tools focus on sexual exploitation, particularly in minors, while other trafficking forms are under-addressed. Sustained training, validated tools, and interdisciplinary collaboration are essential. Systemic barriers must be addressed through clear protocols and institutional commitment to ensure effective, survivor- centered detection and care.
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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 | 0.026 | 0.119 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.033 | 0.031 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".