A review of the literature on sexual and reproductive health of African migrant and refugee children
Bibliographic record
Abstract
BACKGROUND: Migration and involuntary displacement of children and young people have recently become common features of many African countries due to widespread poverty, rapid urbanization, joblessness, and instability that motivate them to seek livelihoods away from their places of origin. With limited education and skills, children become vulnerable socioeconomically, thereby exposing themselves to sexual and reproductive health (SRH) risks. METHODS: Against this background, the authors undertook a scoping review of the existing literature between January and June 2019 to highlight current knowledge on SRH of African migrant and refugee children. Twenty-two studies that met the inclusion criteria were reviewed. RESULTS: The results identified overcrowding and sexual exploitation of children within refugee camps where reproductive health services are often limited and underutilized. They also reveal language barriers as key obstacles towards young migrants' access to SRH information and services because local languages used to deliver these services are alien to the migrants. Further, cultural practices like genital cutting, which survived migration could have serious reproductive health implications for young migrants. A major gap identified is about SRH risk factors of unaccompanied migrant minors, which have received limited study, and calls for more quantitative and qualitative SRH studies on unaccompanied child migrants. Studies should also focus on the different dimensions of SRH challenges among child migrants differentiated by gender, documented or undocumented, within or across national borders, and within or outside refugee camps to properly inform and situate policies, keeping in mind the economic motive and spatial displacement of children as major considerations. CONCLUSION: The conditions that necessitate economic-driven migration of children will continue to exist in sub-Saharan Africa. This will provide fertile grounds for child migration to continue to thrive, with diverse sexual and reproductive health risks among the child migrants. There is need for further quantitative and qualitative research on child migrants' sexual and reproductive health experiences paying special attention to their differentiation by gender, documented or undocumented, within or across national borders and within or outside refugee camps.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 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".