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Record W4380882623 · doi:10.25071/1920-7336.41034

A Community-Based Needs Assessment of Resettled Syrian Refugee Children and Families in Canada

2023· article· en· W4380882623 on OpenAlexafffundvenueabout
Redab Al‐Janaideh, Maarya Abdulkarim, Ruth Speidel, Joanne Filippelli, Tyler Colasante, Tina Malti

Bibliographic record

VenueRefuge Canada s Journal on Refuge · 2023
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsUniversity of Toronto
FundersUniversity of AlbertaWorld Health Organization
KeywordsRefugeeOptimismMental healthLeverage (statistics)MedicineThematic analysisService (business)PsychologyNursingPolitical sciencePsychiatrySociologyQualitative researchSocial psychologyBusinessSocial science

Abstract

fetched live from OpenAlex

A needs assessment was conducted to identify the needs, challenges, and strengths of Syrian refugee children and families resettled in Canada and of services for these refugees. Ten refugee caregivers and 17 service providers were interviewed. Thematic analyses indicated significant needs and challenges experienced by refugees (e.g., persistent mental health issues, lack of in-person support), as well as challenges related to refugee services (e.g., discontinuity of mental health services). Several refugee strengths (e.g., optimism for the future and strong familial ties) and refugee service strengths (e.g., service collaboration) were identified, highlighting refugees’ adaptive capacities and points of service leverage to ensure refugees’ well-being and positive resettlement.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.340

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.003
Science and technology studies0.0100.001
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.001
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 head0.020
GPT teacher head0.318
Teacher spread0.297 · 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

Citations6
Published2023
Admission routes4
Has abstractyes

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