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Record W4388103195 · doi:10.1108/ijmhsc-11-2022-0113

Resilience of refugees and asylum seekers in Canada

2023· article· en· W4388103195 on OpenAlexaffabout
Geneveave Barbo

Bibliographic record

VenueInternational Journal of Migration Health and Social Care · 2023
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsRefugeeMental healthOriginalityContext (archaeology)Psychological resilienceGrey literaturePopularityScope (computer science)Resilience (materials science)NarrativePublic relationsPolitical sciencePsychologySociologyMedicineSocial psychologySocial sciencePsychiatryMEDLINEComputer scienceQualitative researchGeographyLaw

Abstract

fetched live from OpenAlex

Purpose This review aims to examine the literature on refugees’ and asylum seekers’ resilience, its historical evolution, key principles, assumptions and recommendations, while focusing on the Canadian context. Design/methodology/approach A narrative literature review has been applied to this manuscript. This approach allows the integration of a wide scope of literature and perspectives, from academic literature to grey literature (e.g. governmental reports and dissertations). Nevertheless, the limitations of this type of review were also discussed. Findings In spite of the gaining popularity of the resilience lens, which emphasizes an individual’s ability to overcome adversities and stressful events, more work is required for its effective integration into health practice, programs and policies, particularly as it relates to refugees’ and asylum seekers’ mental health care. Originality/value Careful consideration of refugees’ and asylum seekers’ mental health needs and Canadian mental health service delivery and policies is a critical first step in reaching such a goal.

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)0.003
metaresearch head score (Gemma)0.009
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.079
Threshold uncertainty score0.574

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.009
Science and technology studies0.0120.005
Scholarly communication0.0050.002
Open science0.0020.004
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 head0.017
GPT teacher head0.359
Teacher spread0.342 · 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

Citations2
Published2023
Admission routes2
Has abstractyes

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Same venueInternational Journal of Migration Health and Social CareSame topicMigration, Health and TraumaFrench-language works237,207