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Record W7106815320 · doi:10.14288/cjur.v8i2.197636

Syrian Refugees’ Experiences in Canada and the Implications on Mental Health

2022· article· en· W7106815320 on OpenAlexaffabout

Bibliographic record

VenueOpen Collections · 2022
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsMcMaster University
Fundersnot available
KeywordsRefugeeAcculturationMental healthSyrian refugeesIslamophobiaNarrativePopulationDisplaced person

Abstract

fetched live from OpenAlex

The Syrian refugee population represents an unprecedented number of migrants in Canada. Vulnerable citizens and their families sought safety in Canada. Given the influx of refugees in Canada who were forced to leave their home country due to the civil war, the impact of the life adversities they experience as they transition to a new country cannot go unexamined. Thus, this narrative review responds to an integral research question: What are the implications of barriers that Syrian refugees experience in their integration process? The literature review examines the integration of Syrian refugees in Canada by accounting for their traumatic experiences in Syria and their post-migratory experiences during their transition in Canada. The review considers two core themes: (a) the significance of culturally appropriate healthcare services for Syrian refugees’ acculturation in Canada, and (b) how Islamophobia and discrimination that many refugees experience serve as an obstacle in their integration. The study also discusses findings that shed light on such themes’ implications for Syrian refugees’ mental health.

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)0.004
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 score0.047
Threshold uncertainty score0.338

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0170.009
Scholarly communication0.0070.002
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.023
GPT teacher head0.345
Teacher spread0.321 · 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

Citations0
Published2022
Admission routes2
Has abstractyes

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