Designing and Evaluating Scalable Child Marriage Prevention Programs in Burkina Faso and Tanzania: A Quasi-Experiment and Costing Study
Bibliographic record
Abstract
BACKGROUND: A significant number of girls are married as children, which negatively impacts their health, education, and development. Given the sheer numbers of girls at risk of child marriage globally, the challenge to eliminate the practice is daunting. Programs to prevent child marriage are typically small-scale and overlook the costs and scalability of the intervention. IMPLEMENTATION: This study tested and costed different approaches to preventing child marriage in rural Burkina Faso and Tanzania. The approaches tested were community dialogue, provision of school supplies, provision of a livestock asset, a model including all components, and a control arm. A quasi-experimental design was employed with surveys undertaken at baseline and after 2 years of intervention. We examined the prevalence of child marriage and school attendance controlling for background characteristics and stratified by age group. Programmatic costs were collected prospectively. RESULTS: Among those in the community dialogue arm in Burkina Faso, girls aged 15 to 17 years had two-thirds less risk (risk ratio [RR]=0.33; 95% confidence interval [CI]=0.19, 0.60) of being married and girls aged 12 to 14 years had a greater chance of being in school (RR=1.18; 95% CI=1.07,1.29) compared to the control site. In Tanzania, girls aged 12 to 14 years residing in the multicomponent arm had two-thirds less risk of being married (RR=0.33; 95% CI=0.11, 0.99), and girls 15 to 17 in the conditional asset location had half the risk (RR=0.52; 95% CI=0.30, 0.91). All the interventions tested in Tanzania were associated with increased risk of girls 12 to 14 years old being in school, and the educational promotion arm was also associated with a 30% increased risk of girls aged 15 to 17 years attending school (RR=1.3; 95% CI=1.01, 1.67). Costs per beneficiary ranged from US$9 to US$117. CONCLUSION: The study demonstrates that minimal, low-cost approaches can be effective in delaying child marriage and increasing school attendance. However, community dialogues need to be designed to ensure sufficient quality and intensity of messaging. Program managers should pay attention to the cost, quality, and coverage of interventions, especially considering that child marriage persists in the most hard-to-reach rural areas of many countries.
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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 | 0.026 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".