VP190 A Review Of Best Practices In Five Mental Disorders In Youth
Bibliographic record
Abstract
INTRODUCTION: In order to support service planning of the youth program of the East of Montreal Health and Social Services Board, and potentially of the other twenty-five programs across the Quebec province, our hospital-based Health Technology Assessment (HTA) unit was asked to bring evidence of the effective interventions for five most common mental disorders in children and young populations, namely anxio-depressive disorders, attention deficit and hyperactivity disorder, oppositional and conduct disorders, substance abuse disorders, and suicide attempts. METHODS: A review of reviews was conducted for the five disorders in young populations aged 6 to 25 years. This was based exclusively on systematic reviews and meta-analysis of a minimum two randomized-controlled trials. The review was completed with examples of Quebec's good practices in youth mental health gathered from personal research experience of clinical researchers involved in the project. The project involved collaboration with three other hospital units and provincial HTA agencies. RESULTS: No review supporting screening and early detection for the five disorders was identified. Prevention, however, was better covered in the literature, and a clear distinction was made between universal, targeted and indicated interventions. In general, targeted and indicated prevention interventions were effective in the case of anxio-depressive (1) and substance use disorders, while universal prevention strategies seemed to reduce suicide attempts and suicide ideation (2). Effective treatments also exist for these mental disorders. In general, psychotherapies dominated for anxio-depressive and substance use disorders; parental skills dominated in oppositional disorders, whilst pharmacological treatment dominated in attention deficit and hyperactivity disorder (3). Evidence was limited for suicide attempts. The overview of Quebec's good practices allowed identification of interventions or practices already in use in the province. CONCLUSIONS: The review summarized effective interventions for five most common mental disorders in young populations. It also permitted to identify several research gaps, and therefore research recommendations were formulated for the province's health research agency.
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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 | 0.012 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.020 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".