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Record W2997211238 · doi:10.1111/acps.13147

Age at migration and the risk of psychotic disorders: a systematic review and meta‐analysis

2020· review· en· W2997211238 on OpenAlexafffund
Kelly K. Anderson, Jordan Edwards

Bibliographic record

VenueActa Psychiatrica Scandinavica · 2020
Typereview
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsLawson Health Research InstituteWestern University
FundersCanadian Mental Health AssociationLawson Health Research Institute
KeywordsMeta-analysisPsychiatryPsychologySchizophrenia (object-oriented programming)MEDLINEMedicineClinical psychologyPolitical science

Abstract

fetched live from OpenAlex

OBJECTIVE: To conduct a systematic review and meta-analysis of the existing evidence on the association between age at migration and the risk of psychotic disorders. METHODS: Observational studies were eligible for inclusion if they presented data on the association between age at migration and the risk of psychotic disorders among first-generation migrant groups. We used two random effects meta-analyses to pool effect estimates for each stratum of age at migration relative to (i) a native-born reference category and (ii) the youngest age stratum (0 to 2 years). RESULTS: Ten studies met inclusion criteria, and five were included in the meta-analysis. The risk of psychotic disorder among people who migrate prior to age 18 is nearly twice as high as the native-born population, with no evidence of effect modification by age strata. People who migrate during early adulthood (19 to 29 years) have a similar risk of psychotic disorder as the native-born population (IRR = 0.93, 95% CI = 0.60, 1.44) and a lower risk relative to those who migrate during infancy (0 to 2 years) (IRR = 0.58, 95% CI = 0.33, 1.04). CONCLUSIONS: Migrant status is one of few well-established risk factors for psychotic disorder, yet we have limited understanding of the underlying etiology. The findings of this review advance our understanding of this association and identify high-risk groups to target for intervention.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.847
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0080.002
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.032
GPT teacher head0.350
Teacher spread0.317 · 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.

Study designMeta-analysis
Domainnot available
GenreReview

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

Citations50
Published2020
Admission routes2
Has abstractyes

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