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Record W2185545901 · doi:10.3109/09638237.2015.1078881

Migration and young people’s mental health in Canada: A scoping review

2015· review· en· W2185545901 on OpenAlexaffabout
Carla Hilario, John L. Oliffe, Josephine Pui‐Hing Wong, Annette J. Browne, Joy L. Johnson

Bibliographic record

VenueJournal of Mental Health · 2015
Typereview
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsSimon Fraser UniversityToronto Metropolitan UniversityUniversity of British Columbia
Fundersnot available
KeywordsMental healthInclusion (mineral)ResidencePsychologyDiversity (politics)Promotion (chess)Psychological interventionPublic healthMental distressGerontologyHealth promotionIntervention (counseling)PsychiatryMedicineSociologyNursingSocial psychologyPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: Young people's mental health is a public health priority. Given the influences of migration and resettlement on mental health, synthesis of current research with young people from migrant backgrounds can help inform mental health promotion initiatives that account for and are responsive to their needs. AIMS: This article distils the results of a review of published literature on the mental health of adolescent immigrants (ages 10-19) living in Canada. METHOD: Scoping review methods were used to define inclusion and exclusion criteria; inform the search strategies; and extract and synthesize key findings. RESULTS: Fourteen articles met criteria for inclusion. Analysis of the studies indicate diversity in mental health indicators, e.g., mental distress, emotional problems and behavioral problems, as well as a wide range of influences on mental health from age at migration and length of stay to place of residence, income and discrimination. CONCLUSIONS: Findings support the need to account for the array of influences on young people's mental health in relation to migration and to augment initiatives beyond the level of individual 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)0.004
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.754
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.000
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 head0.077
GPT teacher head0.445
Teacher spread0.368 · 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 designOther design
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

Citations41
Published2015
Admission routes2
Has abstractyes

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