Children’s mental health symptoms over three decades (1993–2022): a comparison of population-based cross-sectional samples
Bibliographic record
Abstract
Concerns have been raised about an increase in children's mental health symptoms over the past 30 years, including after COVID-19 lockdowns. Yet, few studies have investigated variations over generations, while considering sex and socioeconomic status. We aimed to address this gap by comparing mental health symptoms (emotional distress, impulsivity/hyperactivity/inattention, disruptive behaviours) reported by classroom teachers of 11-year-olds in three population-based, prospective, representative cohorts in Quebec, Canada. Analyses included 1665 (83%) of the Quebec Longitudinal Study of Kindergarten Children, in 1993; 1305 (62%) of the Quebec Longitudinal Study of Child Development, 2009; and 3871 (100%) of the Quebec Survey of Child Development in Kindergarten, 2022; ~50% boys. Teacher-rated symptoms on the validated Social Behavior Questionnaire showed higher scores of emotional distress and impulsive/hyperactive/inattentive symptoms in 2022 than 2009, and higher in 2009 than 1993 (very small-to-small effect sizes: Cohen's d 0.12 and 0.26 for emotional distress, 0.06 and 0.25 for impulsive/hyperactive/inattentive symptoms, respectively; P < 0.001). Disruptive behaviour symptoms scored lower in 2022 than 2009, though higher in 2009 than 1993, with very small effect sizes (Cohen's d: -0.15 and 0.09, respectively). Boys presented more impulsive/hyperactive/inattentive and disruptive behaviour symptoms than girls; girls showed more emotional distress than boys. Children from economically disadvantaged households (lowest 20% of income distribution) presented higher symptoms rates than advantaged children. These findings provide novel and timely evidence about variations in children's mental health symptom rates over three decades, underscoring the need for preventive interventions as early as elementary school, tailored differentially for boys and socioeconomically disadvantaged children.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".