Learning from COVID-19 to enhance settlement services for refugees to Canada
Bibliographic record
Abstract
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).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.007 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".