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Record W4414276688 · doi:10.7189/001c.131756

“I have broken bones, where should I go now?”: Qualitative research findings on refugees’ journey with injury healthcare

2025· article· en· W4414276688 on OpenAlexfundno aff
Jihad Makhoul, Lea Chaiban, Samar Al‐Hajj

Bibliographic record

VenueJournal of Global Health Economics and Policy · 2025
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsnot available
FundersInternational Development Research CentreArab Council for the Social Sciences
KeywordsRefugeeThematic analysisHealth carePsychological interventionQualitative researchPopulationPublic healthOccupational safety and healthSuicide prevention

Abstract

fetched live from OpenAlex

Background The global refugee crisis presents a major public health challenge, with Syrian refugees in Lebanon facing a heightened injury burden. This population experiences 2.5 times more occupational injuries, with 1 in 5 suffering burns and 1 in 30 sustaining conflict-related injuries, among other trauma types. This study explores refugee injuries to inform targeted interventions and policies. Methods This study builds on the Surgeons Overseas Assessment of Surgical Need framework to explore injured refugees’ perspectives and barriers to healthcare access. An ethnographic-inductive approach was employed, combining direct participation and thematic analysis of interviews. The interviews were conducted in colloquial Arabic with a sample of adult Syrian refugees with unmet surgical or healthcare needs and took place in participants’ dwellings, following an interview guide, and were audio-recorded, transcribed, and analyzed using thematic analysis. Results A total of 17 participants were included. Findings indicate participants live with family members in suboptimal dwellings, have unsustainable work conditions, strained community relationships, and sustained injuries from violence, occupations, and war. The healthcare they have received seemed inadequate, delayed, and limited to specific services, leading to incomplete recovery and adverse impacts on their quality of life. Conclusions Syrian refugees in Lebanon face significant challenges in accessing equitable healthcare for injuries, resulting in prolonged suffering, incomplete recovery, and financial difficulties. Lebanon’s privatized healthcare system, combined with insufficient humanitarian support, exacerbates these barriers. Addressing these issues requires a multifaceted approach, including subsidized healthcare programs, mobile medical units in refugee-dense areas, targeted injury prevention initiatives, and expanding mental health services for injured refugees.

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)0.015
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.018
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0160.014
Scholarly communication0.0070.006
Open science0.0020.010
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0050.000

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 head0.093
GPT teacher head0.540
Teacher spread0.447 · 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 designQualitative
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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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