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Record W4394914376 · doi:10.5430/ijh.v10n1p10

Ukrainian refugee women’s experiences of settlement and navigating health and social services in Canada

2024· article· en· W4394914376 on OpenAlexaffabout
Areej Al‐Hamad, Kateryna Meterskey, Rosanra Yoon, Denise McLane-Davison, Yasin M. Yasin, Caitlin Gare, Molly Hingorani

Bibliographic record

VenueInternational Journal of Healthcare · 2024
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsRefugeeSettlement (finance)UkrainianPolitical scienceHealth servicesEconomic growthGender studiesGeographySociologyBusinessEnvironmental healthMedicineEconomicsPopulationLaw

Abstract

fetched live from OpenAlex

This qualitative descriptive study explores Ukrainian refugee women’s settlement experiences and how they negotiate the social and health care services to support their mental health and well-being in Canada. Utilizing an intersectional lens data from the lived experience of 16 Ukrainian refugee women was thematically analyzed. Four prominent themes emerge from the women’s narratives of their migration and settlement journey – a) confluence of oppressions; b) multifaceted and interwoven paths to cultural integration and adaptation, c) convergence of identity in professional development; and d) navigating settlement. Research findings reveal the complexities of self-reconstruction and socialization as experienced by refugee women. We are of the opinion that hosting refugee women in a new country and providing hope for a new life mean offering them meaningful choices built on forms of affordable and accessible culturally appropriate health and social services and ensuring that their settlement and integration in their new country is successful.

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)0.001
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 score0.250
Threshold uncertainty score0.603

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.018
GPT teacher head0.378
Teacher spread0.360 · 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

Citations5
Published2024
Admission routes2
Has abstractyes

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