Cognitive Outcomes and Academic Performance of Refugee Children in Canadian Schools
Bibliographic record
Abstract
Context Limited research examines how refugee children adapt to Canadian schools and how this influences their cognitive, academic, and psychosocial well-being. Understanding these factors can inform policies and interventions to support refugee children’s integration and long-term success. Objective To assess cognitive outcomes, academic performance, and psychosocial functioning of refugee children in Canada. Study Design and Analysis This is a cross-sectional quantitative study utilizing standardized cognitive and academic assessments. Descriptive and analytical statistics were used to evaluate cognitive performance, verbal and nonverbal abilities, and academic achievement in reading and mathematics. Setting or Dataset The study was conducted in Calgary, Alberta, with participants recruited from the Sunridge Family Medicine Clinic and the Calgary Catholic Immigration Society youth program. Population Studied Refugee children aged 5–12 years, residing in Canada for 1–5 years, and enrolled in kindergarten or elementary school. Intervention/Instrument Cognitive and academic abilities were assessed using the Wechsler Preschool and Primary Scale of Intelligence ® – Fourth Edition CDN (for children under six years) or the Wechsler Intelligence Scale for Children ® – Fifth Edition CDN (for children six years and older). Academic achievement was measured using the Wechsler Individual Achievement Test – Third Edition CDN. Outcome Measures The primary outcome measures included Full-Scale IQ, verbal and nonverbal cognitive abilities, and academic performance in reading and math. Results Thirty-three refugee children participated, with a mean age of 9 years (17 females). Among them, 12 were in kindergarten to Grade 3 and 21 in Grades 4–6, representing nine nationalities. The average Full-Scale IQ was 74 (range: 55–100), significantly below the Canadian norm of 100. Verbal IQ scores were particularly low, while nonverbal scores were relatively stronger. English reading skills were weak, whereas math performance was comparatively better. Most children came from non-English-speaking households. Expected Outcomes Findings highlight significant challenges in verbal cognitive and academic domains among refugee children, emphasizing the need for targeted educational and language support interventions to enhance their academic success and integration into the school system.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".