Schools’ and teachers’ roles and challenges in supporting the mental wellbeing of refugee youths: a qualitative study with Swedish teachers
Bibliographic record
Abstract
PURPOSE: Resettled refugee youths are increasingly entering host-country school systems and are at risk of poor mental wellbeing. Schools and teachers are often expected to provide psychosocial support to youths with refugee backgrounds, but the teachers' views on this expectation are poorly understood. We investigated the question: What do Swedish teachers believe is the role of (1) schools and (2) teachers in supporting refugee youths' mental wellbeing? METHOD: Four semi-structured focus groups were conducted with 30 Swedish educators from five schools. Interview transcripts were analysed and themes reflecting the key concepts were constructed using reflexive thematic analysis. RESULTS: Seven themes were constructed. Three addressed the role of schools in supporting refugee youths' mental wellbeing: Promoting Belonging, Offering Refuge, and Instilling Civic Literacy. Four addressed the role of teachers: Building Relationships with Students, Maintaining a Non-therapeutic Relationship, Connecting to Professionals, and Instructing in the Classroom. CONCLUSION: Teachers believe that both schools and teachers play an important role in supporting refugee youths' mental wellbeing and each contributes in unique ways. However, schools and teachers are not always successful in supporting refugee youth and teachers reported facing challenges such as unclear roles and a lack of resources.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".