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Record W3052745520 · doi:10.1002/jts.22582

Academic Achievement and Psychosocial Adjustment in Child Refugees: A Systematic Review

2020· review· en· W3052745520 on OpenAlexaff
Fariba Aghajafari, Emilie Pianorosa, Zahra Premji, Soheil Souri, Deborah Dewey

Bibliographic record

VenueJournal of Traumatic Stress · 2020
Typereview
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsAlberta Children's HospitalHotchkiss Brain InstituteQueen's UniversityUniversity of Calgary
Fundersnot available
KeywordsPsychosocialRefugeePsychologyPopulationAcademic achievementDevelopmental psychologyClinical psychologyMedicinePsychiatryEnvironmental healthPolitical science

Abstract

fetched live from OpenAlex

Child refugees are at high risk for problems with academic achievement and psychosocial well-being. We aimed to review the literature concerning these outcomes in primary school-aged child refugees. This study was a systematic review and included studies that reported on outcomes of interest in child refugees between 5 and 12 years of age. Our search generated 3,172 articles; we selected 313 for review and included 45. Child refugees are diverse in their educational performance, and early deficits often resolve with time spent in the host country. These children are at an increased risk of emotional and behavioral difficulties, and multiple factors are associated with these outcomes. Although educational difficulties of primary school-aged child refugees in high-income countries tend to resolve, the risks for psychosocial problems persist. This review provides a deepened understanding of the diverse educational and psychosocial experiences of these children and highlights the need for developing health and educational programs to support this population.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.009
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.005
Bibliometrics0.0060.008
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.059
GPT teacher head0.418
Teacher spread0.359 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations28
Published2020
Admission routes1
Has abstractyes

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