Design and Implementation of the Amenah Early Marriage Pilot Intervention Among Syrian Refugees in Lebanon
Bibliographic record
Abstract
<h3>ABSTRACT</h3> Implementing and evaluating interventions in humanitarian settings in low- and middle-income countries presents unique challenges that are little addressed in the implementation literature. We document the process of developing, implementing, and evaluating the Amenah pilot intervention that aimed to mitigate the drivers of early marriage in a Syrian refugee community in Lebanon. Adolescent girls’ vulnerability to early marriage increases following displacement due to poverty, insecurity, and school disruptions. We delineate how, as a local research team, we triangulated evidence from the international literature and formative community research to make informed decisions during the intervention’s design and implementation phases. The pilot was delivered to 203 Syrian refugee schoolgirls aged 11–14 years during the 2017–2018 academic year. It consisted of 16 structured, interactive sessions with girls and a set of facilitated meetings with the girls’ mothers, both of which were implemented by trained female community workers from the Syrian refugee community. Process evaluation results showed that sociodemographic factors predicted attendance among mothers, but relationships with peers in the intervention were the only significant predictor of attendance among girls. The primary outcomes of the pilot were attitudinal measures related to education and marriage. Attitudes toward education were highly positive at baseline and did not change over the course of the intervention. There were no significant changes in girls’ ideal age at marriage. Among girls aged 13 and older at endline, the mean self-reported expected age at marriage increased slightly from 20.2 to 20.8 years (<i>P</i><.05). Our results also suggest that girls may adjust their expected age at marriage downward as they become older and if they drop out of school. We reflect in the discussion on some of the challenges encountered and lessons learned for the benefit of researchers intending to conduct community-based interventions in displacement settings.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".