Legal residency status and its relationship with health indicators among Syrian refugees in Lebanon: a nested cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Failure to possess or renew legal residency permits increases the burden on a vulnerable refugee population. It risks detention or deportation, and hinders access to basic services including healthcare. This study aimed to examine the association between legal residency status and health of Syrian refugees living in Lebanon. METHODS: Data were from two independent nested cross-sectional studies collected in 2022 through telephone surveys. In the first study, all Syrian refugees aged 50 years or older from households that received humanitarian assistance were invited to participate. The second included all adult Syrian refugees residing in a suburb of Beirut. The exposure was self-reported possession of a legal residency permit in Lebanon. The self-reported health outcomes were mental health status, COVID-19 vaccine uptake, and access to needed healthcare services. Separate logistic regression models examined the association between lacking a legal residency permit and each health outcome, adjusted for age, length of stay in Lebanon, education, employment, wealth index and receipt of assistance. RESULTS: The first sample included 3357 participants (median age 58 years (IQR: 54-64), 47% female), of whom 85% reported lacking a legal residency permit. The second sample included 730 participants (median age 34 years (IQR: 26-42), 49% female), of whom 79% lacked a legal residency permit. In both studies, lacking a legal residency permit increased the odds of having poor mental health (adjusted odds ratio (aOR): 1.46 (95% CI: 1.07 to 1.99); aOR: 1.62 (95% CI: 1.01 to 2.60)) and decreased the odds of COVID-19 vaccine uptake (aOR: 0.66 (95% CI: 0.54 to 0.80); aOR: 0.51 (95% CI: 0.32 to 0.81)). In the subsample who needed primary healthcare, lacking a legal residency permit decreased the odds of access to primary healthcare, which was statistically significant in the second study (aOR: 0.37 (95% CI: 0.17 to 0.84)). CONCLUSIONS: The majority of Syrian refugees from these two samples reported lacking a legal residency permit in Lebanon. This was associated with poor mental health and lower uptake of COVID-19 vaccination, potentially originating from fear of detention or deportation. These findings call for urgent action to support access to legal documentation for refugees in Lebanon.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".