Assessing the effect of the Samata intervention on factors hypothesised to be on the pathway to child marriage and school drop-out: results from a cluster-randomised trial in rural north Karnataka, India
Bibliographic record
Abstract
Background We implemented a comprehensive intervention (Samata) to address school drop-out and child marriage among rural, marginalised adolescent girls in north Karnataka, south India. Here, we investigate (i) the impact of the intervention on factors hypothesised at baseline to be on the pathway to preventing school drop-out and child marriage, and (ii) associations between these factors and secondary school completion and child marriage. Methods Data was collected for a cluster-RCT evaluation. Factors hypothesised to be on the pathway to improving secondary school retention and delaying age at marriage included: (i) uptake of skills and training by adolescent girls; (ii) uptake of government school scholarships by families of adolescent girls; (iii) gender equitable attitudes among girls; (iv) reduced harassment by boys; and (v) an enabling school environment. Analyses used individual-level cluster-RCT survey data, were intention-to-treat and used mixed-effects logisitic regression models. Results 92.6% (2257/2457) of girls participated at baseline (13-14 years) and 72.8% (1788/2457) participated at end-line (15-16 years). At end-line, uptake of skills and training, gender equitable attitudes around marriage, and recent harassment by boys were significantly higher among girls in the intervention arm but there was no difference in uptake of government school scholarships, gender equitable attitudes around education or eve-teasing, or an enabling school environment by trial arm. Out-of-school/married girls were significantly less likely to have accessed skills or training, to have attended empowerment groups or to have made new friends (past year). They had lower levels of self-efficacy and were twice as likely to report having no hope for the future compared with their in-school/unmarried counterparts. Conclusions Samata was implemented in a context of substantial secular change across India; impacting on some of the factors hypothesised to be on the pathway was not sufficient to improve secondary school retention or delay marriage beyond what was already occurring. School dropout and child marriage were associated with diminished opportunities and well-being among girls. Targeted interventions are still needed; learnings from our study can be used to inform future interventions which similarly aim to impact on child marriage and secondary school retention within programmatic timeframes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".