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Record W2995343486 · doi:10.1177/1049732319895241

Mental Health Services for Syrian Refugees in Lebanon: Perceptions and Experiences of Professionals and Refugees

2020· article· en· W2995343486 on OpenAlexaff
Hala Kerbage, Filippo Marranconi, Yara Chamoun, Alain Brunet, Sami Richa, Shahaduz Zaman

Bibliographic record

VenueQualitative Health Research · 2020
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsMcGill University
FundersEconomic and Social Research Council
KeywordsRefugeeMental healthPsychological interventionPerceptionQualitative researchDistressMedicineDisplaced personNursingPsychologyPsychiatryPolitical scienceSociologyClinical psychology

Abstract

fetched live from OpenAlex

We applied semi-structured and in-depth interviews to explore the perceptions and experiences of 60 practitioners/policymakers and 25 Syrian participants involved in mental health services for refugees in Lebanon. Refugees were found to view their distress as a normal shared reaction to adversity while professionals perceived it as symptomatic of mental illness. Practitioners viewed Syrian culture as an obstacle to providing care and prioritized educating refugees about mental health conditions. Policymakers invoked the state of crisis to justify short-term interventions, while Syrian refugees requested community interventions and considered resettlement in a third country the only solution to their adverse living conditions. The therapeutic relationship seems threatened by mistrust, since refugees change their narratives as an adaptive mechanism in response to the humanitarian system, which professionals consider manipulative. We discuss the implications of our findings for mental health practice in humanitarian settings.

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)0.006
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.072
Threshold uncertainty score0.456

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.251
GPT teacher head0.611
Teacher spread0.360 · 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.

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".

Quick stats

Citations50
Published2020
Admission routes1
Has abstractyes

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