Emerging Mental Health Diagnoses and School Disruption: An Examination Among Clinically Referred Children and youth
Bibliographic record
Abstract
Previous research linking school disruption with mental health problems has largely relied on assessments of academic achievement to measure school disruption. Early disruptive classroom behaviour (e.g., conflict with school staff, negative attitudes toward school), however, may precipitate poor academic performance and may stem from emerging mental health concerns, particularly among young children. To address this gap in the literature, 912 clinically referred children and youth (ages 4–18 years old) were assessed using the interRAI Child and Youth Mental Health (ChYMH) assessment utilizing a cross-sectional study design. The ChYMH assessment evaluates school disruption independently of academic achievement, and includes a comprehensive assessment of the child’s mental health functioning, needs, and preferences. A logistic regression analysis revealed that various provisional mental health diagnoses (i.e., attention-deficit/hyperactivity disorder, disruptive behaviour, mood disorders, and, to a lesser extent, anxiety) were associated with disruption in the classroom. Implications for school-based care planning are 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".