355 Social determinants of mental health for young people from refugee backgrounds: reflections from young people and their family members
Bibliographic record
Abstract
Abstract OP 27: Refugees and Asylum Seekers 3, B210 (FCSH), September 4, 2025, 16:00 - 17:00 Aims While a range of social determinants of health have been identified in relation to mental health for people from refugee backgrounds, there is relatively little research that has comprehensively examined these determinants for refugee youth specifically nor considered the perspectives of family members. This paper reports qualitative findings from a multidisciplinary, mixed methods research study examining social determinants of mental health (SDMH) for refugee youth in Australia. Methods This qualitative component involved interviews to date with 45?young people from refugee backgrounds aged 13-26 from a range?of cultural backgrounds and migration pathways who had experienced mental health challenges, and 30 family members (parents and siblings) of young people in this group. Interviews were conducted face-to-face and online and explored key SDMH for refugee youth. Interviews were transcribed and analysed thematically. Results Key factors identified by young people themselves as affecting their mental health included developing English language skills, belonging (fitting in and experiences of racism and discrimination), balancing family responsibilities with their own integration, their parents’ health, and pressure to do well in a new country. These factors incorporated issues associated with finances, employment, and family separation and played out in family relationships and at school and in neighbourhoods, and also reflected the impacts of immigration and settlement and related policies. Family member participants reiterated similar themes in relation to their young family member, with most also reporting significant challenges to their own mental health. Parents in particular found it difficult to talk about mental health challenges for their children. Conclusions Findings reported here are feeding into a co-design process with refugee young people, families and other stakeholders to develop and pilot a short SDMH refugee youth screening tool to assist services and community organisations identify key SDMH for individuals in order to better support mental wellbeing.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".