Interviewing Asylum-Seeking Children: A Scoping Review of Research to Inform Best Practices
Bibliographic record
Abstract
Immigration interviews with asylum-seeking youth have been largely understudied. In domestic legal settings, children interviewed about abuse and maltreatment provide more detailed, relevant responses when asked open-ended questions and when interviewed in a neutral environment, among other supportive practices. In asylum settings, guidance for interviews with youth derives from the United Nations Convention on the Rights of the Child. It is not clear to what extent best practices are employed during asylum interviews with youth. This scoping review was performed to (a) provide an overview of empirical literature on interviews with children in immigration settings, including border screenings, interviews with representatives, and asylum hearings, (b) explore whether best practices derived from forensic psychology and children's rights are observed in asylum interviews, (c) identify unique interview needs of asylum-seeking youth, and (d) derive implications for research and practice. A scoping review of three databases conducted in October 2023 yielded titles, of which 29 articles met inclusion criteria. These comprised quantitative and qualitative studies in English from 2003 to 2023. Three articles identified were quantitative, and 26 were qualitative. While several articles touched on interview practices and youth's experiences of interviews, only a few examined how asylum-seeking youth responded to different interview factors such as question type and interview setting. Key findings highlight inconsistent application of best practice principles, and several areas where best practices to support asylum-seeking children require clarification through further research.
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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 | 0.073 | 0.186 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.044 | 0.040 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".