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Record W3217711310 · doi:10.32920/ihtp.v1i3.1483

Older Syrian Refugees’ Experiences of Language Barriers in Postmigration and (re)settlement Context in Canada.

2021· article· en· W3217711310 on OpenAlexaffvenueabout
Souhail Boutmira

Bibliographic record

VenueInternational Health Trends and Perspectives · 2021
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsRefugeeContext (archaeology)FluencyPsychologyLanguage barrierPsychological interventionQualitative researchConvictionMedicineGerontologyCriminologyPolitical scienceSociologyPsychiatryGeographySocial science

Abstract

fetched live from OpenAlex

This research explores older Syrian experiences of language barriers in Canada's post-migration and resettlement context. Guided by the ecosystemic model, the qualitative descriptive method was used to describe the experiences of six older adults (three women and three men, 55-year-old and over) living in the Greater Toronto-Hamilton Area. Results suggest the lack of English proficiency affects refugees' resettlement and, in particular, older adults at risk of abuse. Language barriers influence older adult refugees because it limits their ability to navigate the Canadian systems, exacerbate their dependency on adult children, increase social isolation, and decrease employment and income opportunities. Participants' commitment to learning English comes from their conviction that fluency has an essential role in shaping their experiences in Canada. Conclusions can help policymakers identify specific interventions to address language barriers among older adult 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)0.003
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score0.181

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0130.006
Scholarly communication0.0030.001
Open science0.0010.005
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 head0.015
GPT teacher head0.353
Teacher spread0.338 · 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 routes3
Has abstractyes

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