A systematic review of drivers and interventions against sex work migration in Edo State, Nigeria
Bibliographic record
Abstract
Purpose Sex work migration involves a huge number of females from Nigeria, and has attracted concerns within and across the country. To add to ongoing conversations about responsible migration, our review underscores the prevalence of sex work migration in Edo State, Nigeria, the drivers and interventions. Design/methodology/approach The review adopted exhaustive search terms coined with the aid of “Boolean Operators”. Search terms were entered into several search engines and databases to elicit peer-reviewed and grey literature within sex work migration and human trafficking for commercial sex. An output of 578 studies was recorded with 76 (43 academic papers and 33 grey literature) meeting the inclusion criteria. Findings The study acknowledged wide-spread prevalence of sex work migration involving Nigerian females who are largely from Edo State. It achieved a prioritization of the factors that drive sex work migration based on how frequent they were mentioned in reviewed literature: economic (64.4%), cultural (46%), educational (20%), globalization (14.5%) and political factors (13.2%). Several interventions were highlighted together with their several limitations which include funding, absence of grass-roots engagement, dearth of appropriate professionals, corruption, weak political will, among others. A combination of domestic and international interventions was encouraged, and social workers were found to be needful. Originality/value Our systematic review is the first on this subject, as none was found throughout our search. It seeks to inform policy measures and programmes, as well as horizontal efforts poised to tackle the rising figures of sex work migrants and attendant consequences in Nigeria.
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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 | 0.014 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".