Understanding the developmental well-being of children from refugee backgrounds in British Columbia, Canada: A population-level mixed methods approach
Bibliographic record
Abstract
Objectives and ApproachThis study utilized an explanatory sequential mixed methods approach to investigate the developmental well-being of children from refugee backgrounds in British Columbia (BC), Canada. Objective 1 (quantitative) leveraged population-level, government administrative data individually linked to a province-wide, routinely collected, teacher-reported measure of children’s development in kindergarten (the Early Development Instrument; EDI) to examine developmental outcomes across five domains for children identified as first-generation refugees (N=770), first-generation immigrants (N=7875), and non-migrants (N=199,186). In Objective 2 (qualitative), the population-level EDI results were brought to focus groups with BC educators and settlement workers (N=7) who work closely with children from refugee backgrounds to further corroborate, expand, and elaborate on the findings. ResultsA series of multiple linear regression models; adjusted for age, sex, and English Language Learner status showed that first-generation refugee status was significantly predictive of lower EDI scores in the areas of language & cognitive development, communication & generation knowledge, social competence, emotional maturity, and physical health & well-being. Focus group results corroborated the quantitative findings, added critical complexity/context (e.g., impacts of trauma), and identified important policy-oriented levers (e.g., early, accessible assessments and supports). ConclusionsThe study provided an understanding of the population-level developmental well-being of children from refugee backgrounds in BC, framed by rich, contextualized, and actionable knowledge from focus groups. ImplicationsShowcasing the combined breadth and depth of using a mixed methods approach, how we can best support the developmental challenges and build upon the strengths of children from refugee backgrounds will be discussed.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".