Child-street migration among HIV-affected families in Kenya: a mediation analysis from cross-sectional data
Bibliographic record
Abstract
Within Kenya, an estimated quarter of a million children live on the streets, and 1.8 million children are orphaned. In this study, we analyze how HIV contributes to the phenomenon of child-street migration. We interviewed a random community sample of caregiving women (n = 1974) in Meru County, Kenya, using a structured questionnaire in summer 2015. Items included reported HIV prevalence of respondent and her partner, social support, overall health, school enrollment of biologically related children and whether the respondent has a child currently living on the streets. Controlling for alcohol use, education, wealth, age and household size, we found a positive-graded association between the number of partners living with HIV and the probability that a child lives on the street. There was little difference in the odds of a child living on the street between maternally affected and paternally affected households. Lower maternal social support, overall health and school enrollment of biologically related children mediated 14% of the association between HIV-affected households and reporting child-street migration. Street-migration of children is strongly associated with household HIV, but the small percentage of mediated effect presents a greater need to focus on interactions between household and community factors in the context of HIV. Programs and policies responding to these findings will involve targeting parents and children in HIV-affected households, and coordinate care between clinical providers, social service providers and schools.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".