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Premigration Exposure to Political Violence Among Independent Immigrants and Its Association With Emotional Distress

2004· article· en· W2025110204 on OpenAlexaffabout
Cécile Rousseau, Aline Drapeau

Bibliographic record

VenueThe Journal of Nervous and Mental Disease · 2004
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsMcGill University
Fundersnot available
KeywordsRefugeeImmigrationPersecutionDistressPoliticsPopulationPolitical scienceEmotional distressPolitical violenceCriminologyDemographic economicsPsychologySocial psychologyMedicineClinical psychologyPsychiatryEnvironmental healthLawAnxietyEconomics

Abstract

fetched live from OpenAlex

Although the distinction between independent immigrants and refugees has an impact on policy, services, and public opinion because it implies differences in resettlement needs, few recent studies have documented the validity of this assumption. In this population-based survey of recent migrants in Quebec (N = 1871), immigration status (refugee, independent, or sponsored immigrant) is examined in relation to premigration exposure to political violence and refugees' emotional distress, assessed with the SCL-25. A higher percentage of refugees reported exposure to political violence in their homeland, but the percentages of exposed independent (48%) and sponsored (42%) immigrants were unexpectedly high. Emotional distress was significantly higher among Chinese respondents who had witnessed acts of violence and in subjects from Arab countries who reported persecution. These results suggest that service providers and policy makers should not assume that independent immigrants have not been exposed to political violence before their migration.

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)0.000
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 score0.073
Threshold uncertainty score0.145

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.009
GPT teacher head0.273
Teacher spread0.265 · 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

Citations79
Published2004
Admission routes2
Has abstractyes

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