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Record W3195628041 · doi:10.18192/aporia.v13i2.6016

Immigration, settlement process and mental health challenges of immigrants/ refugees: Alternative care thinking

2021· article· en· W3195628041 on OpenAlexaffvenueabout
Margareth Santos Zanchetta, Abinet Gebreegziabher Gebremariam, David Ansari, Elizabeth Huang, Stéphanie Larchanché, Clément Picot-Ngo, Marguerite Cognet, Shone John

Bibliographic record

VenueAporia · 2021
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsToronto Metropolitan University
FundersInstitut National de la Santé et de la Recherche Médicale
KeywordsRefugeeImmigrationMultidisciplinary approachMental healthSettlement (finance)Vulnerability (computing)Health careEconomic growthPolitical scienceSociologyMedicineNursingPsychiatrySocial scienceBusiness

Abstract

fetched live from OpenAlex

This paper discusses progressive thinking and clinical views on improving mental health practice for immigrants and refugees. It addresses policy, care delivery, professionals’ attitudes, and immigrants’ access to mental health care — all factors especially pertinent for practice in major immigration hubs. The data was gathered from invited presentations and discussions among participants at an international multidisciplinary symposium, including health and social scientists from Toronto (Canada) and Paris (France), major urban centres attracting large numbers of immigrant and refugees who constantly encounter challenges for their successful settlement. The focus is on alternative care thinking and innovative approaches for better care and understanding of these populations’ health behavior. Recommendations on how to advance knowledge relevant for these two urban hubs of immigration were documented, underpinned by the consensus that economic disparities, societal and political forces, as well as cultural and linguistic factors, influence immigrants’ and refugees’ vulnerability regarding mental health stability.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.179
Threshold uncertainty score0.517

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.019
GPT teacher head0.353
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations2
Published2021
Admission routes3
Has abstractyes

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