Displacement and Emotional Well-Being among Married and Unmarried Syrian Adolescent Girls in Lebanon: An Analysis of Narratives
Bibliographic record
Abstract
Lebanon hosts over one million refugees displaced from Syria as a result of the armed conflict-of whom, approximately 15% are adolescents aged between 12 and 17 years of age. Many female adolescent migrants report a decrease in quality of life and an increase in family tensions. This study sought to investigate the emotional well-being of adolescent Syrian girls in Lebanon. We hypothesized that married girls may experience additional hardships and thus greater feelings of dissatisfaction in daily life, given their young marriage and responsibilities at home. This study was part of a large mixed-methods study on the experiences of Syrian refugee girls in Lebanon (n = 1422). Using line-by-line coding and thematic analysis, 188 first-person narratives from Syrian girls were analysed. Our results highlight poor emotional well-being among married and unmarried girls, with sadness, fear and anger commonly mentioned. Some participants expressed feelings of hope, happiness, gratefulness and empowerment. Unmarried girls (n = 111) were more likely to associate their shared stories with negative feelings such as sadness (47% vs. 22%), disappointment (30% vs. 19%), and frustration (32% vs. 22%) than were married girls (n = 77). Four themes emerged as important determinants: access to education, perceived safety, peer support, and longing for life back in Syria. Continued efforts to improve emotional well-being for married and unmarried refugee girls are needed in Lebanon, in particular those that address the nuances for these groups.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".