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Record W76191825 · doi:10.1177/070674371305800406

Age at Immigration to Canada and the Occurrence of Mood, Anxiety, and Substance Use Disorders

2013· article· en· W76191825 on OpenAlexafffundvenueabout
Beth Patterson, Hmwe Hmwe Kyu, Katholiki Georgiades

Bibliographic record

VenueThe Canadian Journal of Psychiatry · 2013
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsCanadian Institutes of Health ResearchMcMaster University
FundersMcMaster University
KeywordsAnxietyPsychiatryImmigrationMoodMood disordersPsychologyAnxiety disorderSubstance useClinical psychologyMedicineHistory

Abstract

fetched live from OpenAlex

OBJECTIVE: The process of migration and resettlement has been associated with increased risk for psychiatric illness. Our study sought to examine the association between age at immigration and risk for mood, anxiety, and substance use disorders (SUDs) among adult immigrants in Canada. METHOD: Data from the Canadian Community Health Survey: Mental Health and Well-Being, a cross-sectional study of psychiatric disorder conducted in 2002, was used to identify a representative sample of adult immigrants in Canada (n = 4946). Logistic regression was used to examine the association between age at immigration (0 to 5 years, 6 to 17 years, and 18 years and older) and 12-month prevalence of mood and anxiety disorders, and SUDs. RESULTS: Immigrants who arrived prior to age 6 years reported the highest risk for mood (OR 3.41; 95% CI 1.7 to 7.0) and anxiety disorders (OR 6.89; 95% CI 3.5 to 13.5), compared with those who immigrated at the age of 18 years or older, after adjusting for covariates, including duration of residence. CONCLUSIONS: Younger age at immigration was associated with increased risk of having a current mood disorder, anxiety disorder, or SUD. These findings speak to the importance of developing and evaluating targeted prevention programs for young immigrant children and adolescents.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.118
Threshold uncertainty score0.223

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.010
GPT teacher head0.239
Teacher spread0.229 · 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 teacher head, 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

Citations48
Published2013
Admission routes4
Has abstractyes

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