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Record W4367627665 · doi:10.3390/healthcare11091295

Structural Origins of Poor Health Outcomes in Documented Temporary Foreign Workers and Refugees in High-Income Countries: A Review

2023· review· en· W4367627665 on OpenAlexafffund
Borum Yang, Clara Kelly, Isdore Chola Shamputa, Kimberley Barker, Duyên Thi Kim Nguyêñ

Bibliographic record

VenueHealthcare · 2023
Typereview
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsUniversity of New BrunswickSaint John Regional HospitalDalhousie University
FundersDalhousie University
KeywordsCINAHLImmigrationThematic analysisHealth equityContext (archaeology)RefugeeHealth careMEDLINEPolitical scienceCountry of originDemographic economicsMedicinePsychologyGerontologyEconomic growthQualitative researchGeographySociologyEconomicsSocial science

Abstract

fetched live from OpenAlex

Despite growing evidence of racial and institutional discrimination on minoritized communities and its negative effect on health, there are still gaps in the current literature identifying health disparities among minoritized communities. This review aims to identify health barriers faced by relatively less studied migrant subgroups including documented temporary foreign workers and refugees residing in high-income Organisation for Economic Co-operation and Development (OECD) countries focusing on the structural origins of differential health outcomes. We searched Medline, CINAHL, and Embase databases for papers describing health barriers for these groups published in English between 1 January 2011 and 30 July 2021. Two independent reviewers conducted a title, abstract, and full text screening with any discrepancies resolved by consensus or a third reviewer. Extracted data were analyzed using an inductive thematic analysis. Of the 381 articles that underwent full-text review, 27 articles were included in this review. We identified housing conditions, immigration policies, structural discrimination, and exploitative labour practices as the four major emerging themes that impacted the health and the access to healthcare services of our study populations. Our findings highlight the multidimensional nature of health inequities among migrant populations and a need to examine how the broader context of these factors influence their daily experiences.

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: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.691
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.067
GPT teacher head0.446
Teacher spread0.379 · 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 designSystematic review
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

Citations6
Published2023
Admission routes2
Has abstractyes

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