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Record W2014370344 · doi:10.1111/imig.12116

Sudanese and Somali Refugees in Canada: Social Support Needs and Preferences

2013· article· en· W2014370344 on OpenAlexaffabout
Edward Makwarimba, Moira Stewart, Laura Simich, Knox Makumbe, Edward Shizha, Sharon Anderson

Bibliographic record

VenueInternational Migration · 2013
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsUniversity of AlbertaWilfrid Laurier UniversityAlberta Health Services
Fundersnot available
KeywordsSomaliRefugeeSocial supportPsychological interventionIntervention (counseling)PsychologyEconomic growthSocioeconomicsPolitical scienceNursingSociologyMedicineSocial psychology

Abstract

fetched live from OpenAlex

Abstract The aim of the study was to identify the unique support needs and preferences of African refugees in Canada. In‐depth interviews were conducted with Sudanese and Somali refugees (n=68) living in two cities in central and western Canada. Refugees were interviewed individually to identify their support needs, current sources of support, available support programmes, barriers to access to support resources, and preferred support interventions. These refugees reported major support needs, depleted social networks, and barriers to accessing services and supports. They identified distinct preferences for support from peers from the same country of origin and professionals. Participants wanted group‐level support supplemented by one‐to‐one support. Transportation, child care, meals, and peers matched by language and gender were recommended to enhance accessibility to support programmes. These findings can inform the design of support intervention research and enhance the relevance and supportiveness of services and programmes for recent 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)0.001
metaresearch head score (Gemma)0.001
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 score0.120
Threshold uncertainty score0.241

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.001
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.001
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 head0.018
GPT teacher head0.300
Teacher spread0.282 · 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

Citations93
Published2013
Admission routes2
Has abstractyes

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