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Record W2336942659 · doi:10.1097/nmd.0000000000000516

Mental Health Effects of Premigration Trauma and Postmigration Discrimination on Refugee Youth in Canada

2016· article· en· W2336942659 on OpenAlexafffundabout
Morton Beiser

Bibliographic record

VenueThe Journal of Nervous and Mental Disease · 2016
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsToronto Metropolitan UniversitySt. Michael's Hospital
FundersCanadian Institutes of Health Research
KeywordsRefugeeMental healthImmigrationPovertyPsychologyMedicineClinical psychologyPsychiatryPolitical science

Abstract

fetched live from OpenAlex

This report examines the role of pre- and post-migration trauma in explaining differences in refugee and immigrant mental health. Data were derived from mother-youth refugee and immigrant dyads from six countries of origin who were living in Canada at the time of the study. Youth reports of emotional problems (EP) and aggressive behavior (AB) were the mental health outcomes. EP and AB were regressed on predictor blocks: a) status (refugee versus immigrant), visible minority, and gender; b) premigration trauma and postmigration discrimination; c) parent and youth human and social capital; d) poverty, neighborhood, and schools. Refugees suffered higher levels of EP and AB, premigration traumas, and discrimination. Postmigration perception of discrimination predicted both EP and AB and explained immigrant versus refugee differences in EP. Antirefugee discrimination net of discrimination based on immigrant or visible minority status has deleterious mental health consequences.

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)0.002
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 score0.045
Threshold uncertainty score0.090

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.002
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 head0.011
GPT teacher head0.277
Teacher spread0.266 · 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

Citations192
Published2016
Admission routes3
Has abstractyes

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