MétaCan
Menu
Back to cohort
Record W6991553566

The Healthy Immigrant Effect in Canada: A Systematic Review

2015· article· en· W6991553566 on OpenAlexaboutno aff

Bibliographic record

VenueScholarship@Western (Western University) · 2015
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationHuman capitalVariation (astronomy)Mental healthSelection (genetic algorithm)Public health
DOInot available

Abstract

fetched live from OpenAlex

Canada’s immigration admissions policy calls for individuals with high human capital (Knowles, 2007). Given the strong links between human capital and health (Jasso et al., 2004) and previous research which suggested the presence of a seemingly universal foreign-born health advantage among Canada’s migrant population, we expected to see the healthy immigrant effect across the life-course and for multiple health outcomes. What we found instead was a pattern much more complex than previously envisioned. Our review uncovered a clear survival advantage for immigrants, owing in part to positive self and state selection processes (at least for non-refugee migrants). However, there is greater variation in the healthy immigrant effect for morbidity. Moreover, viewed through the lens of different life-course stages, we uncovered a strong foreign-born health advantage in adulthood but less so during the perinatal period, childhood/adolescence, and late life. Immigrant selection may be less relevant for the very young and very old, and of course we should thus not expect the presence of a healthy immigrant effect for these groups if that is the case. But even during adulthood when the healthy immigrant effect appears to be most effective, some discrepancies still remain between different immigrant subgroups depending on the type of health measure used (e.g., greater variation for self-rated health but less variation for mental health, disability/functional limitations, risk behaviors, and chronic conditions).

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.868
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.085
GPT teacher head0.346
Teacher spread0.261 · 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 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

Citations44
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueScholarship@Western (Western University)Same topicMigration, Health and TraumaFrench-language works237,207