Learning Problems in Children of Refugee Background: A Systematic Review
Bibliographic record
Abstract
CONTEXT: Learning problems are common, affecting up to 1 in 10 children. Refugee children may have cumulative risk for educational disadvantage, but there is limited information on learning in this population. OBJECTIVE: To review the evidence on educational outcomes and learning problems in refugee children and to describe their major risk and resource factors. DATA SOURCES: Medline, Embase, PubMed, Cumulative Index to Nursing and Allied Health Literature, PsycINFO, and Education Resources Information Center. STUDY SELECTION: English-language articles addressing the prevalence and determinants of learning problems in refugee children. DATA EXTRACTION: Data were extracted and analyzed according to Arksey and O'Malley's descriptive analytical method for scoping studies. RESULTS: Thirty-four studies were included. Refugee youth had similar secondary school outcomes to their native-born peers; there were no data on preschool or primary school outcomes. There were limited prevalence data on learning problems, with single studies informing most estimates and no studies examining specific language disorders or autism spectrum disorders. Major risk factors for learning problems included parental misunderstandings about educational styles and expectations, teacher stereotyping and low expectations, bullying and racial discrimination, premigration and postmigration trauma, and forced detention. Major resource factors for success included high academic and life ambition, "gift-and-sacrifice" motivational narratives, parental involvement in education, family cohesion and supportive home environment, accurate educational assessment and grade placement, teacher understanding of linguistic and cultural heritage, culturally appropriate school transition, supportive peer relationships, and successful acculturation. LIMITATIONS: Studies are not generalizable to other cohorts. CONCLUSIONS: This review provides a summary of published prevalence estimates for learning problems in resettled refugee children, highlights key risk and resource factors, and identifies gaps in 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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".