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Record W4402944358 · doi:10.1101/2024.09.27.24314488

Legal residency status and its relationship with health indicators among Syrian refugees in Lebanon: a nested cross-sectional study

2024· preprint· en· W4402944358 on OpenAlexfundno aff
Marie‐Elizabeth Ragi, Hala Ghattas, Berthe Abi Zeid, Hazar Shamas, Noura El Salibi, Sawsan Abdulrahim, Jocelyn DeJong, Stephen J. McCall

Bibliographic record

VenuemedRxiv · 2024
Typepreprint
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsnot available
FundersForeign, Commonwealth and Development OfficeChina Academy of Engineering PhysicsNational Institute for Health and Care ResearchInternational Development Research CentreEnhancing Learning and Research for Humanitarian AssistanceWellcome Trust
KeywordsCross-sectional studyRefugeeSyrian refugeesEnvironmental healthPolitical sciencePsychologyMedicineGeographyDemographic economicsLawEconomicsPathology

Abstract

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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 examined 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 the needed healthcare services. Separate adjusted logistic regression models examined the association between lacking a legal residency permit and each health outcome. 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):1.62 (95%CI:1.2-2.2); aOR:1.62 (95%CI:1.01-2.60)], and decreased the odds of COVID-19 vaccine uptake [aOR:0.64 (95%CI:0.53-0.78); aOR:0.51 (95%CI:0.32-0.81)]. In the sub-sample who needed primary healthcare, lacking a legal residency permit decreased the odds of access to primary healthcare in the second study only (aOR:0.37 (95%CI:0.17-0.84)). 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. Key Messages What is already known on this topic Refugees are a vulnerable population and face varied challenges, such as marginalization and high levels of poverty. The lack of legal residency increases the risk of detention or deportation and may impact access to essential public services and healthcare. What this study adds This study showed that the majority of Syrian refugees in Lebanon lacked legal residency permits and this impacted receipt of the COVID-19 vaccination during the pandemic and their mental health. How this study might affect research, practice or policy These findings highlight the need for actions to support access for legal documentation for refugees and enable equitable access to vaccination campaigns and health and mental health services for this vulnerable population.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.039
GPT teacher head0.382
Teacher spread0.343 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

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