Mental health of children with special health needs in Manitoba, Canada
Bibliographic record
Abstract
Children with special health needs (SHN) may be more likely to experience mental disorders compared to their peers without SHN. The aim of this study was to establish and contextualize the risk of mental disorders among children flagged as having SHN at school entry. We linked data from the Early Development Instrument (EDI), a teacher-completed questionnaire of children’s developmental health in kindergarten, collected in Manitoba between 2006 and 2015, with provincial health administrative data up to 2019. Using binary logistic regressions, we examined the odds of receiving a diagnosis of any mental disorder, by categories of SHN: special needs, impairments in physical, vision/hearing, learning, speech, behaviour, and emotions, needing further assessment, and having 2+ SHN categories. Of the 36,462 children with EDI data linked to health administrative data, 5,882 (16.1%) were identified as having a SHN in kindergarten. The odds of developing a mental disorder varied by subtype of SHN. Children needing further assessment, with special needs, 2+ SHN categories, or with a learning, behavioural, or emotional impairment had between 1.35 and 3.27 times the odds of receiving a mental health diagnosis than their peers without these issues. Having a behavioural impairment increased a child’s odds the most. Having a physical, visual, or hearing impairment was not associated with a mental disorder. Having a special needs designation and impairments in behaviour and emotions in kindergarten puts children at risk of a future mental disorder. These findings may help generate wider mental health supports in schools for children with SHN.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".