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Record W2503213658 · doi:10.1177/1043659616660361

Theoretical Perspectives on Issues and Interventions Related to Migrant Resettlement in Canada

2016· article· en· W2503213658 on OpenAlexaffabout
Ameneh Toosi, Solina Richter, Boris Woytowich

Bibliographic record

VenueJournal of Transcultural Nursing · 2016
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsNorQuest CollegeUniversity of Alberta
Fundersnot available
KeywordsAcculturationPsychological interventionSettlement (finance)Perspective (graphical)SociologyPolitical sciencePsychologyEconomic growthMedicineImmigrationBusinessNursingEconomicsComputer scienceLaw

Abstract

fetched live from OpenAlex

International migration has increased at a significant rate over the past several decades as many migrants relocate in the hope of finding better living conditions. Unfortunately, not all migrants realize their dreams but instead find themselves in poor living conditions and are less satisfied with their health and well-being. The purpose of this article is to explore the factors that influence the integration of migrants into a new culture through two theoretical lenses: transitions theory and acculturation theory. The authors propose that acculturation and transition are influenced by factors at both the societal and individual level and therefore interventions aimed at promoting successful integration should be focused at both those levels. This article adds a new perspective to the migrant health framework and offers a new approach for researchers, clinicians, and program developers. The overall health and well-being of migrants may improve by focusing on individual factors that contribute to successful settlement through predeparture or early arrival preparation programs.

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)0.006
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.133
Threshold uncertainty score0.795

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0230.019
Scholarly communication0.0110.003
Open science0.0040.006
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0100.000

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 head0.022
GPT teacher head0.368
Teacher spread0.347 · 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 designTheoretical or conceptual
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

Citations0
Published2016
Admission routes2
Has abstractyes

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