Impact of Family Loss and Separation on Refugee Youth: Implications for Policy and Programs: Scoping Review
Bibliographic record
Abstract
Background: Refugee youth separated from their families comprise a socially vulnerable population. The barriers in accessing social and healthcare services escalates their trauma. This review maps the extent, volume and attributes of the existing literature around the impacts of separation on refugee youth in terms of their mental health and wellbeing. Methodology: A systematic search strategy and PRISMA-ScR model was adopted to review 112 peer-reviewed articles from three major databases and open-source journals to find 32 eligible articles. Charted data was analyzed through reflexive Thematic Analysis to answer research questions on patterns, impacts, policy supports and potential solutions for unaccompanied refugee youths. Results: About 92% of the research focused on separated refugee youth, whereas 8% focused on guardians. The majority of the research on this topic was conducted in EU countries, the remainder being from Canada, USA, UK, and Australia. Most of the youth participants in the research were male. Themes included prevalence of mental health issues (e.g. PTSD, Depression, and Anxiety, etc.), their predictors (e.g. stress, number of traumatic experiences, etc.) and effective interventions. Effective interventions included psychotherapy, art therapy and cognitive behavioural therapy. Without intervention, mental health issues persisted or got worse over time. Social empowerment opportunities, material resources and guardian support skills served as protective factors to youth mental health. Conclusion: The review identified the extent of current research and scope for further research. The next step is to collect data from refugee youth with lived experience and their service providers (through a co-design approach) to develop a ‘Service Toolkit’ and advocacy materials for policy implications.
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 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 | 0.014 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| 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".