Profiles of children's social–emotional health at school entry and associated income, gender and language inequalities: a cross-sectional population-based study in British Columbia, Canada
Bibliographic record
Abstract
OBJECTIVES: Early identification of distinct patterns of child social-emotional strengths and vulnerabilities has the potential to improve our understanding of child mental health and well-being; however, few studies have explored natural groupings of indicators of child vulnerability and strengths at a population level. The purpose of this study was to examine heterogeneity in the patterns of young children's social and emotional health and investigate the extent to which sociodemographic characteristics were associated. DESIGN: Cross-sectional study based on a population-level cohort. SETTING: All kindergarten children attending public schools between 2004 and 2007 in British Columbia (BC), Canada. PARTICIPANTS: 35 818 kindergarten children (age of 5 years) with available linked data from the Early Development Instrument (EDI), BC Ministry of Health and BC Ministry of Education. OUTCOME MEASURE: We used latent profile analysis (LPA) to identify distinct profiles of social-emotional health according to children's mean scores across eight social-emotional subscales on the EDI, a teacher-rated measure of children's early development. Subscales measured children's overall social competence, responsibility and respect, approaches to learning, readiness to explore, prosocial behaviour, anxiety, aggression and hyperactivity. RESULTS: Six social-emotional profiles were identified: (1) overall high social-emotional functioning, (2) inhibited-adaptive (3) uninhibited-adaptive, (4) inhibited-disengaged, (5) uninhibited-aggressive/hyperactive and (6) overall low social-emotional functioning. Boys, children with English as a second language (ESL) status and children with lower household income had higher odds of membership to the lower social-emotional functioning groups; however, this association was less negative among boys with ESL status. CONCLUSIONS: Over 40% of children exhibited some vulnerability in early social-emotional health, and profiles were associated with sociodemographic factors. Approximately 9% of children exhibited multiple co-occurring vulnerabilities. This study adds to our understanding of population-level distributions of children's early social-emotional health and identifies profiles of strengths and vulnerabilities that can inform future intervention efforts.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".