Adjustment of refugee children and adolescents in Australia: outcomes from wave three of the Building a New Life in Australia study
Bibliographic record
Abstract
BACKGROUND: High-income countries like Australia play a vital role in resettling refugees from around the world, half of whom are children and adolescents. Informed by an ecological framework, this study examined the post-migration adjustment of refugee children and adolescents 2-3 years after arrival to Australia. We aimed to estimate the overall rate of adjustment among young refugees and explore associations with adjustment and factors across individual, family, school, and community domains, using a large and broadly representative sample. METHODS: Data were drawn from Wave 3 of the Building a New Life in Australia (BNLA) study, a nationally representative, longitudinal study of settlement among humanitarian migrants in Australia. Caregivers of refugee children aged 5-17 (N = 694 children and adolescents) were interviewed about their children's physical health and activity, school absenteeism and achievement, family structure and parenting style, and community and neighbourhood environment. Parent and child forms of the Strengths and Difficulties Questionnaire (SDQ) were completed by caregivers and older children to assess social and emotional adjustment. RESULTS: Sound adjustment according to the SDQ was observed regularly among young refugees, with 76-94% (across gender and age) falling within normative ranges. Comparison with community data for young people showed that young refugees had comparable or higher adjustment levels than generally seen in the community. However, young refugees as a group did report greater peer difficulties. Bivariate and multivariate linear regression analyses showed that better reported physical health and school achievement were associated with higher adjustment. Furthermore, higher school absenteeism and endorsement of a hostile parenting style were associated with lower adjustment. CONCLUSIONS: This is the first study to report on child psychosocial outcomes from the large, representative longitudinal BNLA study. Our findings indicate sound adjustment for the majority of young refugees resettled in Australia. Further research should examine the nature of associations between variables identified in this study. Overall, treating mental health problems early remains a priority in resettlement. Initiatives to enhance parental capability, physical health, school achievement and participation could assist to improve settlement outcomes for young refugees.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".