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Record W2940193946 · doi:10.1002/jcop.22183

Acculturation and adjustment of migrants reporting trauma: The contextual effects of perceived ethnic density

2019· article· en· W2940193946 on OpenAlexaffabout
Tomas Jurcik, Momoka Sunohara, Esther Yakobov, Ielyzaveta Solopieiva‐Jurcikova, Rana Ahmed, Andrew G. Ryder

Bibliographic record

VenueJournal of Community Psychology · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicRacial and Ethnic Identity Research
Canadian institutionsJewish General HospitalMcGill UniversityConcordia University
Fundersnot available
KeywordsAcculturationMainstreamEthnic groupPsychologyContext (archaeology)DistressImmigrationClinical psychologySocial psychologySociologyPolitical scienceGeographyAnthropology

Abstract

fetched live from OpenAlex

Little is known about the relation between acculturation and socioecological contexts of migrants with a personal trauma history living in the community. This study represents an extension of our previous work and aimed to unpack the perceived neighborhood ethnic density (ED) effect and examine the moderating role of ED on the acculturation-adjustment relation in a community sample of migrants with trauma (N = 99) from developing countries residing in Montreal, Canada. ED was protective against general psychological distress but did not predict posttraumatic symptoms. The ED effect was mediated via degree of acculturation to the French-Canadian mainstream cultural context, rather than heritage acculturation, social support, or discrimination. Moreover, protective effects of French-Canadian mainstream acculturation for depressive symptoms and life satisfaction were found under high but not low ED conditions. Similarities and differences with our previous research as well as theoretical and prevention implications are discussed from a person-environment interaction perspective.

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)0.003
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 score0.059
Threshold uncertainty score0.117

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.125
GPT teacher head0.461
Teacher spread0.336 · 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

Citations13
Published2019
Admission routes2
Has abstractyes

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Same venueJournal of Community PsychologySame topicRacial and Ethnic Identity ResearchFrench-language works237,207