‘Now, She’s a Child and She Has a Child’—Experiences of Syrian Child Brides in Lebanon after Early Marriage
Bibliographic record
Abstract
This study examined the lived experiences of Syrian refugee child brides to understand their needs as they navigate new social roles after marriage. A cross-sectional study was conducted in Lebanon using SenseMaker® to collect narratives from married Syrian girls age 13 and older and from their parents. Thematic analysis using inductive coding was performed. Identified themes were organized according to an adaptation of Bronfenbrenner’s socioecological theory of human development to present experiences across all levels of the girls’ interactions and potential influences. Themes at the microsystem level included overwhelming domestic expectations and worry about their own children in the girls’ roles as young mothers. Experiences of intimate partner violence and family conflict were common. At the exosystem level, participants described safety concerns and financial and legal system challenges. The macrosystem level highlighted social expectations around married girls discontinuing education and around separation or divorce. As efforts continue to prevent child marriage within the Syrian crisis and globally, understanding experiences of already married girls is critical to providing support for mitigating harm to child brides. Programs might consider safety planning, parenting supports, access to skills training and education, peer-to-peer social networking, and engaging husbands or families of child brides.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".