Preventing mental disorders in children: a public health priority.
Bibliographic record
Abstract
BACKGROUND: Mental disorders affect 14% of children, cause significant long-term disability and are arguably the leading health problems that Canadian children face after infancy. Treatment services alone cannot meet children's mental health needs. In addition to treatment, prevention programs hold potential to reduce the number of children with disorders in the population. Effective programs exist for preventing conduct, anxiety and depressive disorders, three of the most prevalent disorders in children. Therefore, we investigated the state of Canadian programs in comparison with prevention programs described in the literature for these three disorders. METHODS: We identified children's mental health and early child development (ECD) programs across Canada with national or provincial/territorial scope and significance and with potential relevance to mental health. We then interviewed policy-makers to determine which programs included goals related to mental health, and incorporated key features from programs known to be effective for preventing the three disorders of interest. RESULTS: No prevention programs specific to children's mental health were identified. However, 17 ECD programs incorporated generic goals related to mental health and incorporated key features seen in effective prevention programs. Only Ontario's Better Beginnings, Better Futures (BBBF) explicitly included mental health within its major program goals, incorporated multiple features seen in effective (conduct disorder) prevention programs and demonstrated positive child mental health outcomes. DISCUSSION: The lack of Canadian prevention programs specific to children's mental health is concerning. ECD programs have the potential to improve child mental health outcomes within their wider mandates. BBBF is an exemplar for such programs. However, new investments in implementing (and evaluating) programs that specifically aim to prevent mental disorders are required to improve the mental health of children in the population. Preventing children's mental disorders must be a Canadian public health priority.
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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 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".