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Record W2054039958 · doi:10.1177/0020764013486750

Mental health of Latin Americans in Canada: A literature review

2013· review· en· W2054039958 on OpenAlexafffundabout
Jorge Ginieniewicz, Kwame McKenzie

Bibliographic record

VenueInternational Journal of Social Psychiatry · 2013
Typereview
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
FundersCanadian Institutes of Health Research
KeywordsLatin AmericansMental healthImmigrationPersecutionRefugeeDiversity (politics)PopulationMental illnessMedicineCultural diversityGerontologyPoliticsPolitical sciencePsychiatryEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: Latin Americans represent one of the fastest-growing immigrant populations in Canada. But very little is known about their mental health. AIMS: This paper reviews the literature on the mental health of Latin American immigrants to Canada. The paper also identifies potential areas to expand the research agenda. METHOD: Twenty-five papers were identified by a comprehensive electronic search undertaken in medical- and humanities-related databases. RESULTS RESULT: s are reported in three sections: (1) the rates of mental illness; (2) the risk factors that affect mental health; and (3) the access and barriers to care and services. Findings indicate that despite the diversity of immigration from Latin America to Canada, much of the information on mental health focuses on Central American refugees. The most frequently examined risk factor is displacement as a consequence of political persecution and torture in the home country. Access to mental health services in this population seems to be limited by cultural differences and language barriers. CONCLUSION: New research on this topic should reflect the growing diversity and heterogeneity of the Latin American population in Canada.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.624
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.039
GPT teacher head0.413
Teacher spread0.374 · 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 designNot applicable
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

Citations28
Published2013
Admission routes3
Has abstractyes

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