Divergent experiences of Syrian refugee girls in Tripoli, Lebanon: a cross-sectional analysis to inform response and programming
Bibliographic record
Abstract
Abstract Background In prior studies exploring the experiences of Syrian refugee girls in Tripoli, Beqaa, and Beirut, especially in relation to child marriage, our data showed distinct variations in response patterns from Tripoli relative to the other regions. To gain a deeper understanding of these contrasting experiences and to ascertain the unique needs of Syrian refugees in Tripoli, we conducted a mixed-methods analysis. Methods We conducted a mixed-methods, cross-sectional study in Lebanon in July–August 2016 using Cognitive Edge’s SenseMaker®. Participants were self-identified Syrian refugees and Lebanese men aged 14 and older. For dyad questions, Kruskal–Wallis H tests with chi-squared statistics compared different geographic subgroups and post-hoc Fisher’s Least Squares Difference analysis identified which groups differed. For triad data, geometric means and 95% confident ellipses were used to compare subgroups. Qualitative analysis of narratives from Tripoli complemented the quantitative results. Results We collected 1422 narratives from 1346 unique participants, including 464 participants from Tripoli. From the quantitative data, three significant response pattern differences between participants in Tripoli and participants in other locations were noted: (a) Syrian fathers in Tripoli perceived that additional programs/services were needed to support Syrian girls, (b) men in Tripoli reported that financial insecurity had contributed to the experiences of Syrian girls, and (c) Syrian girls and mothers in Tripoli perceived that girls were ‘protected too much’. These differences may be driven by the economic and political subnational differences unique to Tripoli. Conclusions Our subnational analysis identified a need for programs and services, financial security, and safety for Syrian girls in Tripoli. Different perspectives in Tripoli highlight that the experiences, concerns, and needs of Syrian refugees in Lebanon are not homogenous across locations. Localized contextualization is needed to guide the development of tailored approaches to support and to inform the distribution of resources.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".