Assessing school readiness domains in a large cohort of refugee children: Validation and links with family factors
Bibliographic record
Abstract
Despite the growing global presence of refugee populations, few validated tools exist to assess early learning and development in these contexts. This study examined the use of the International Development and Early Learning Assessment (IDELA) with a large sample of 1033 refugee children aged 4–6 years living in Malaysia, a non-resettlement, low- to middle-income country. Using Rasch modelling, we evaluated the psychometric properties of IDELA and found strong person and item reliability, acceptable item fit, and good evidence of unidimensionality, although some item redundancy was observed. Further, children's school readiness scores were significantly associated with child gender and age, as well as maternal and paternal demographic characteristics (age, education, literacy), but not father employment or occupation type. These findings provide preliminary validation for IDELA’s use in refugee settings and underscore its potential as a culturally adaptable, low-cost tool for assessing development in underserved populations. • Psychometric properties of the International Development and Early Learning Assessment (IDELA) tool was examined with a large sample of refugee children. • Rasch modelling demonstrated excellent person and item reliability and separation. • Item fit statistics confirmed IDELA’s good unidimensionality and construct validity, with some redundancy noted among items. • Correlational analyses indicated significant associations between Rasch person’s measures with key parent and child variables.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".