Mental health disorders among children with special health needs: A population-based cohort study using linked administrative data from Manitoba, Canada
Bibliographic record
Abstract
OBJECTIVE: An estimated 15-22% of Canadian kindergarten-age children have a special health need (SHN), defined as a clinical diagnosis, a functional need requiring special accommodation at school, or a health condition leading to increased needs. Children with SHN may be more likely to experience mental health disorders than their peers without SHN, placing them at risk for further health and academic challenges. Our objective was to determine the odds of children with SHN identified in kindergarten being diagnosed with a mental health disorder by age 16. METHODS: In this retrospective cohort study using population-based, linked administrative data, we identified children with SHN born 1995-2020 in Manitoba, Canada, and enrolled in kindergarten from 2006-2011. The SHN designation is derived from the Early Development Instrument. We measured prevalence of common childhood mental health disorders (ADHD, mood/anxiety disorders, conduct disorders) in children with SHN to age 16. Using binary logistic regressions, we calculated crude odds ratios (OR) for children with vs. without SHN being diagnosed with a mental health disorder, then adjusted for age, sex, and neighbourhood-level income. RESULTS: Among 42,766 children, 13.8% had a SHN designation in kindergarten. Among these, 41.0% were diagnosed with a mental health disorder by age 16. The odds of a mental health diagnosis by SHN category were: special needs designation in kindergarten (OR 1.75, 95%CI 1.53-2.01); learning impairment (OR 1.61, 95%CI 1.39-1.86); behavioural impairment (OR 3.27, 95%CI 2.87-3.72); and emotional impairment (OR 2.01, 95%CI 1.75-2.32). Children with SHN (vs. none) had higher odds of a mental health disorder if they had 1 + impairment (OR 1.67, 95%CI 1.50-1.85). Adjusting for sociodemographic characteristics did not change the estimates. CONCLUSIONS: The study highlights important kindergarten predictors of future mental health disorders in children, which should be used to inform preventive and supportive strategies for children with SHN and help generate wider mental health supports in schools.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".