Settlement Workers in Schools’ (SWIS) Support for K-12 Refugee Students: A Resilience and Compassion-Based Approach
Bibliographic record
Abstract
The number of refugees worldwide has reached approximately 32.5 million, 41% of whom are children and youth under 18 eighteen years of age (UNHCR, 2022). Between 2015-2021, Canada welcomed 218,430 refugees, with over 87,795 being Syrian (Statistics Canada, 2022). With an estimated 87,000 refugee children and youth in Canada (UNHCR, 2022), I engaged with Settlement workers in schools (SWIS) in Ontario, Canada to explore how they identify newcomer refugee K-12 students’ needs, the challenges SWIS experience, and the strategies they draw on to support newcomer refugee students. Settlement workers in schools identified newcomer refugee students had language learning, social, and psychological needs. The challenges SWIS experience include navigating their relationship with schools, resources, intercultural competence at schools, and professional development. The strategies they use to support newcomer refugee students are broadly categorized under individual, family, school, community, and societal supports. As such, I describe the unique role of SWIS as “compassionate connectors” who support newcomer refugee students based on a holistic approach which includes promoting resilience at multiple levels and a compassion-based framework in schools. It is through collaboration with schools, that SWIS play a key role in enhancing intercultural competence of school staff which aids in promoting integration, a sense of belonging, and well-being for newcomer refugee students.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.023 | 0.011 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".