Collaborating with community-based services to promote evidence-based practice: Process description of a national initiative to improve services for youth with mental health and substance use problems.
Bibliographic record
Abstract
Many youth with significant mental health (MH) and/or substance use (SU) difficulties do not receive specialized services. Collaboration between service providers, researchers, and other stakeholders is essential to improve youth service system capacity to provide evidence-based services to meet the complex array of needs of youth. Facilitators and barriers of implementing evidence-based practice have been identified, but few studies provide examples of the processes of collaboration and implementation for youth MH services. This study explicates the design features and implementation processes of a project to improve screening activities in youth services. These processes supported the building of 16 collaborative networks of service providers from diverse youth-serving sectors (e.g., MH, youth justice, child welfare) in urban, rural, suburban, and remote Canadian communities. These cross-sectoral networks implemented an evidence-based practice (screening youth aged 12-24 years for MH and SU problems using the Global Assessment of Individual Needs-Short Screener [GAIN-SS]) across their services. Materials and resources were provided by a centralized research team. Core project components were standardized and adherence to these components was monitored. Over 800 service providers participated in cross-sectoral networks, capacity-building events, joint data analysis, or interpretation and recommendation sessions. Across the 89 participating agencies, service providers for 84% of participating youth implemented the evidence-based practice accurately in accordance with project protocols, with 98% of positive screens reviewed and addressed according to organizational protocols. Service provider feedback is reported. Facilitators, barriers, and implications of promoting implementation of evidence-based practices across sites and sectors are discussed. (PsycINFO Database Record
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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 | 0.116 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.005 | 0.025 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".