Labor trafficking among migrant populations: a scoping review and qualitative interview study with stakeholders
Bibliographic record
Abstract
Introduction Labor trafficking is a widespread yet underreported form of exploitation that disproportionately affects migrant populations. Migrants often face coercion, deception, and abuse across various labor sectors, and these circumstances are often exacerbated by structural inequalities and legal protections. There is a pressing need to synthesize existing knowledge and integrate stakeholders’ perspectives to inform future prevention and policies. Objective This study examines the scope, nature, and thematic trends in the literature on labor trafficking among migrant populations and augments these findings with qualitative insights from stakeholders working directly with trafficked migrant populations. Methods A scoping review was conducted following A scoping review was conducted according to the PRISMA guidelines. Nineteen peer-reviewed empirical studies published between 2014 and 2024 were analyzed across four databases. In parallel, 17 semi-structured interviews were conducted with service providers, researchers, and advocates experienced in labor trafficking. Structural violence theory guided the thematic analysis using Quirkos software. Results Seven major themes emerged: (1) informal recruitment networks, (2) hazardous and exploitative working conditions, (3) psychological and physical coercion, (4) economic and structural vulnerability, (5) gender-based vulnerabilities, (6) inadequate legal protections and enforcement gaps, and (7) mental health impacts. The qualitative data highlight the lived realities and systemic challenges trafficked migrants face and help validate and enrich the findings from the scoping review. Conclusion Labor trafficking among migrants is driven by economic, legal, and social vulnerabilities. Comprehensive, survivor-centered policies are urgently needed: reforms to recruitment practices, expanded mental health support, and stronger legal frameworks. The study findings call for cross-sectoral collaboration and the integration of migrant voices in program and policy design to combat trafficking for labor exploitation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".