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Record W3138282482 · doi:10.1111/ijsw.12476

Storytelling as a tool: A family‐based intervention for newly resettled Syrian refugee children

2021· article· en· W3138282482 on OpenAlexaffabout
Kim Roger Abi Zeid Daou, Léa Roger Abi Zeid Daou, Maxime Cousineau‐Pérusse

Bibliographic record

VenueInternational Journal of Social Welfare · 2021
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsMcGill University
Fundersnot available
KeywordsRefugeeIntervention (counseling)AnxietyMental healthStressorPsychologyAgency (philosophy)MedicinePsychiatryPolitical scienceSociology

Abstract

fetched live from OpenAlex

Abstract The conflict in Syria has resulted in a humanitarian emergency and one of the largest refugee crises in history. The Canadian government has welcomed over 40,000 Syrian refugees. Stressors caused by instability, conflict, and the resettlement process put refugee children at high risk for mental health problems. Anxiety is a common problem experienced by refugee children. Thus, early intervention is crucial to promote their adequate adaptation and development. This study explores the impact and value of a culturally specific family‐based storybook intervention for newly resettled Syrian refugee children. Six refugee families participated. Anxiety symptoms were measured before and after the intervention, and families shared their experiences, thoughts, and feedback regarding the intervention. The results showed a significant decrease in children's anxiety symptoms. Furthermore, qualitative analyses demonstrated that the intervention was culturally relevant to Syrian refugee families and that it was effective in promoting children's overall well‐being, agency, and family connectedness.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.024
GPT teacher head0.365
Teacher spread0.341 · 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".

Quick stats

Citations12
Published2021
Admission routes2
Has abstractyes

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