Achieving holistic care for refugees: The experiences of educators and other stakeholders in Surrey and Greater Vancouver, Canada
Bibliographic record
Abstract
In 2020, the global number of refugees reached record levels, pressuring asylum countries to determine more effective methods for facilitating integration. This article explores an array of stakeholder practices towards refugees in Surrey and Greater Vancouver, Canada. It is based on questionnaires and interviews that elicit the perceptions and struggles of 40 settlement workers, health and mental health professionals, Members of Parliament, educators, librarians, scholars and grassroots organisations, who work with refugees. The findings show that stakeholders often feel isolated, ‘working in silos’ and wasting time and money due to uncoordinated services and a lack of interagency communication. They feel it is also unreasonable to expect Government‐Assisted Refugees (GARs) to learn English and complete job training in preparation for independent living within 1 year of support. Both refugee adults and children suffer from high levels of trauma, often compounded by interrupted or no schooling. Since education is essential to refugee success, I argue that teachers play a role in filling the gap, often uniquely positioned to form ongoing, safe and trusting relationships with refugee students and their families. For many teachers, it is an ethos of care, compassion and social justice acquired in teacher education programmes that increases refugee resilience, sense of belonging and wellbeing. This article identifies what new collaborations between teachers and other stakeholders might accomplish, including communication back to government policymakers. Recommendations encompass initiating online registries of services and low‐cost housing in neighbourhoods where community schools and services are interlinked, possibly achieving holistic care for all refugees.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.036 | 0.009 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
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".