MétaCan
Menu
Back to cohort
Record W3012516581 · doi:10.1016/j.ssmph.2020.100571

Human rights in countries of origin and the mental health of migrants to Canada

2020· article· en· W3012516581 on OpenAlexafffundabout
Marie-Pier Joly, Blair Wheaton

Bibliographic record

VenueSSM - Population Health · 2020
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health ResearchGeorg-August-Universität GöttingenDeutsche Forschungsgemeinschaft
KeywordsHuman rightsMental healthContext (archaeology)StressorMetropolitan areaDemographic economicsPsychologyPolitical scienceGeographyEconomicsPsychiatryLaw

Abstract

fetched live from OpenAlex

This study explores the effect of human rights violations in countries of origin on migrants' mental health, using archival data on human rights violations from 1970-2011, merged to a representative probability sample of 2412 adults living in a large Canadian metropolitan area. The context of exit is defined at the country level, as opposed to self-reported individual experiences of trauma. While most studies start from a question about direct exposure to human rights violations, they may miss the effect of the national-level social context - threat, instability, disruption of lives, and uncertainty - on mental health. Findings indicate that high levels of human rights violations in countries of origin have long-term effects on migrants' mental health. The impact of human rights violations is substantially explained by the combined effect of stressors both before and after migration, suggesting a cumulative process of stress proliferation following this context of exit.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.020
Threshold uncertainty score0.117

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0050.002
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0000.001
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 head0.030
GPT teacher head0.365
Teacher spread0.334 · 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

Citations8
Published2020
Admission routes3
Has abstractyes

Explore more

Same venueSSM - Population HealthSame topicMigration, Health and TraumaFrench-language works237,207