The Epidemiology of Young People’s Work and Experiences of Violence in Nine Countries: Evidence from the Violence against Children Surveys
Bibliographic record
Abstract
Globally, 497 million young people (15-24 years) are in the labour force. The current research on work and violence indicates reciprocal links across the life course. This study draws on data from 35,723 young people aged 13-24 years in the Violence Against Children Surveys (VACS) in nine countries to describe the epidemiology of work in order to explore associations between (1) current work and violence and (2) childhood violence and work in a hazardous site in young adulthood. The prevalence of past-year work among 13-24-year-olds was highest in Malawi: 82.4% among young men and 79.7% among young women. In most countries, young women were more likely to be working in family or domestic dwellings (range: 23.5-60.6%) compared to men (range: 8.0-39.0%), while men were more likely to be working on a farm. Work in a hazardous site was higher among young men compared to women in every country. Among children aged 13-17 years, we found significant positive associations between past-year work and violence among girls in three countries (aORs between 2.14 and 3.07) and boys in five countries (aORs 1.52 to 3.06). Among young people aged 18-24 years, we found significant positive associations among young women in five countries (aORs 1.46 to 2.61) and among young men in one country (aOR 2.62). Associations between childhood violence and past-year work in a hazardous site among 18-24-year-olds were significant in one country among girls and in three countries among boys. Continued efforts are needed to prevent hazardous work, improve work environments, and integrate violence prevention efforts into workplaces.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".