The Samata intervention to increase secondary school completion and reduce child marriage among adolescent girls: results from a cluster-randomised control trial in India
Bibliographic record
Abstract
BACKGROUND: Secondary education and delayed marriage provide long-term socio-economic and health benefits to adolescent girls. We tested whether a structural and norms-based intervention, which worked with adolescent girls, their families, communities, and secondary schools to address poverty, schooling quality and gender norms, could reduce secondary school drop-out and child marriage among scheduled-caste/scheduled-tribe (SC/ST) adolescent girls in rural settings of southern India. METHODS: ). Primary trial outcomes were proportion of girls who completed secondary school and were married, by trial end-line (15-16 years). Analyses were intention-to-treat and used individual-level girl data. RESULTS: 92.6% (2275/2457) girls at baseline and 72.8% (1788/2457) at end-line were interviewed. At end-line, one-fourth had not completed secondary school (control = 24.9%; intervention = 25.4%), and one in ten reported being married (control = 9.6%; intervention = 10.1%). These were lower than expected based on district-level data available before the trial, with no difference between these, or other schooling or sexual and reproductive outcomes, by trial arm. There was a small but significant increase in secondary school entry (adjusted odds ratio AOR = 3.58, 95% confidence interval CI = 1.36-9.44) and completion (AOR=1.54, 95%CI = 1.02-2.34) in Vijayapura district. The sensitivity and attrition analyses did not impact the overall result indicating that attrition of girls at end-line was random without much bearing on overall result. CONCLUSIONS: Samata intervention had no overall impact, however, it added value in one of the two implementation districts- increasing secondary school entry and completion. Lower than expected school drop-out and child marriage rates at end-line reflect strong secular changes, likely due to large-scale government initiatives to keep girls in school and delay marriage. Although government programmes may be sufficient to reach most girls in these settings, a substantial proportion of SC/ST girls remain at-risk of early marriage and school drop-out, and require targeted programming. Addressing multiple forms of clustered disadvantage among hardest to reach will be key to ensuring India "leaves no-one behind" and achieves its gender, health and education Sustainable Development Goal aspirations. TRIAL REGISTRATION: ClinicalTrials.gov registration number NCT01996241.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".