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Record W4412178024 · doi:10.1186/s13031-025-00680-2

Exploring factors that shaped Syrian refugees integration into Lebanon’s national health system using Kingdon’s Multiple Streams Framework

2025· article· en· W4412178024 on OpenAlexaff
Fadi El‐Jardali, Gladys Honein‐AbouHaidar, Lama Bou-Karroum, Sabine Salameh, Sarah Parkinson, Rima Majed

Bibliographic record

VenueConflict and Health · 2025
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsMcMaster University
FundersEconomic and Social Research CouncilMedical Research CouncilWellcome Trust
KeywordsPublic healthHealth services researchRefugeeSTREAMSSyrian refugeesEpidemiologyEnvironmental healthGeographyPolitical scienceMedicineComputer scienceArchaeologyNursingPathology

Abstract

fetched live from OpenAlex

BACKGROUND: Since the start of the Syrian conflict in 2011, neighboring country Lebanon has hosted the largest number of refugees per capita in the world. To meet refugees' health care needs, Lebanon adopted an integrated model of care. This paper explores the key factors and events that have shaped the policy on the integration of Syrian refugees into the Lebanese national health system through a policy analysis. METHODS: The research team adopted a qualitative approach that employed in-depth interviews with 12 key informants (2 ministers, 4 non-governmental organizations, 3 advocacy group representatives, and 3 healthcare managers) and document review. Thematic framework analysis was used to analyze the data guided by the Kingdon's Multiple Streams Framework. RESULTS: Problem factors that influenced Lebanese health policy towards Syrian refugees include the sheer number of refugees with urgent health care needs who entered a fragile, highly privatized health care system, and political and sectarian dissension around the refugee issue, both of which contributed to a slow government response. In the policy stream, international non-governmental organizations concerned with refugee health started to engage with local authorities. In December 2014, the Lebanon Crisis Response Plan strategy was issued by the government and various partners that iterated the strategy to respond to Syrian refugees' needs. Under the political stream, Lebanon's historical experience with Palestinian refugees, and specifically concerns regarding fear of domiciliation, influenced the unofficial implementation of a 'no camp policy' strategy at the onset of the crisis, which in turn shaped healthcare integration. Further, international non-governmental organizations joined efforts to fund and supplement health care services, while think tank policy organizations advocated for refugees right to healthcare and host community support. CONCLUSION: This study highlights the role of global actors, such as UNHCR, WHO among others, as the main entrepreneurs in integrating refugees into the Lebanese health care system. It also underscored the ad-hoc non-systematic approach with which the policies around refugee health response were made in Lebanon and the influence of political factors. Although the mutual benefits to both host and refugee communities were recognized, many challenges threaten integration, foremost among them the model's financial sustainability.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.483
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.253
GPT teacher head0.425
Teacher spread0.172 · 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 teacher head, not a consensus.

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

Citations1
Published2025
Admission routes1
Has abstractyes

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