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Record W4416768772 · doi:10.1108/ijmhsc-08-2024-0091

Learning from COVID-19 to enhance settlement services for refugees to Canada

2025· article· en· W4416768772 on OpenAlexaffabout
Abe Oudshoorn, Fawziah Rabiah-Mohammed, C. Susana Caxaj, Sarah Benbow, Victoria M. Esses, Jennifer Williamson, Eman Arnout

Bibliographic record

VenueInternational Journal of Migration Health and Social Care · 2025
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsFanshawe CollegeWestern University
Fundersnot available
KeywordsRefugeeSettlement (finance)Government (linguistics)PopulationGratitudeQualitative researchHuman settlementImmigrationLimiting

Abstract

fetched live from OpenAlex

Purpose The aim of this study is to gain knowledge about the strengths and limitations of the Canadian service delivery model in meeting the needs of the government assisted refugee (GAR) population during the COVID-19 pandemic. Design/methodology/approach This study used a qualitative case study design. Findings This aggregated case narrative identified several challenges, including an overwhelming sense of disconnection from friends and family, cultural services, access to information and access to health and social services. These complex experiences have been captured under the following themes: 1) “We do not know, yet”: Living in a Void; 2) “Limited choice”: Finding Services in a Virtual World; 3) “Between four walls”: Missing Connections; 4) “Thank you”: Staff Carrying a Burden of Compassion. Research limitations/implications This study follows a case study design that includes local responses from a small number of refugees from the Middle East. Although a small sample, this research offers detailed and rich knowledge about the settlement experience during a global pandemic in a mid-size Canadian city. This in-depth knowledge may be transferable to other contexts such as settlement of refugees in other regions across Canada. Because this study was undertaken at the beginning of the pandemic, some interviews took place online, limiting information sharing due to a lack of in-person interaction. Some settlement staff expressed reservations around sharing their experiences of providing services during the pandemic. Similarly, some newcomer families underscored their gratitude to migrate to Canada and may not have felt comfortable critiquing the system. Practical implications For settlement services going forward, having a hybrid model of services would ease the settlement experience for refugees because a combination of virtual and in-person support was noted by our participants as an effective and preferred approach. For federal and provincial governments, prioritizing internet connectivity immediately upon arrival is a key to mitigate the isolation and bewilderment that families may experience, especially amidst public health restrictions. Social implications Providing a timeline for time-sensitive information as it occurs for GARs, so GAR families and settlement workers are prompted to seek/deliver just-in-time information necessary for participants is essential to making the experience of settlement less daunting. Nation-wide implementation of the Welcome Group program is a practical step to promote a positive integration and reduce social isolation (West London Welcome, 2023). Cultural ambassadors can help with learning the language, getting to know the neighbourhood and mitigating the impact of social isolation better than if refugee families were left on their own with basic support from settlement agencies. Originality/value Recommendations for settlement service providers, and municipal, provincial and federal governments, are discussed to adapt to a mixed in-person and virtual service-delivery environment. In terms of better preparing and supporting refugees through the arrival process, there is potential to review the education and information settlement services provide for newcomers on an organizational level, including a hybrid education model, resources in more languages, attention to key timing and literacy accessibility (i.e. written, audio and video materials).

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)0.005
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.087
Threshold uncertainty score0.429

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0270.007
Scholarly communication0.0070.002
Open science0.0030.010
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0130.001

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 head0.420
Teacher spread0.402 · 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 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".

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Citations0
Published2025
Admission routes2
Has abstractyes

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