Teacher perceived mental and learning problems of children referred to a school mental health service.
Bibliographic record
Abstract
OBJECTIVE: Delivering mental health services to children and their families through schools has many potential advantages. However, little is known about the characteristics of children referred to such services. This study aimed to determine the pattern of mental health and learning difficulties of children referred to one school mental health service. METHODS: An identity stripped administrative database of all new referrals (n=353) to a school mental health program in southern Alberta between September 2006 and June 2009 was used. Teacher Strengths and Difficulties Questionnaire responses and questions about learning and other developmental problems were included. RESULTS: Hyperactivity-inattention was the most prevalent mental health concern, and spelling was the most common learning concern. Higher rates of hyperactivity-inattention concerns and pro-social deficits were observed for boys and more emotional problems were observed for girls. Hyperactivity-inattention was higher at lower grades. Hyperactivity-inattention and conduct problems were often comorbid as were several learning problems. CONCLUSION: Understanding the typical patterns of concerns among referrals to school mental health services may guide the prioritization of assessment and intervention approaches within these programs. Findings suggest assessments and interventions for ADHD and other disruptive behaviours should be prioritized, as well as the provision of cognitive and academic testing.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".