Disseminating Evidence-Based Preventive Interventions to Promote Wellness and Mental Health in Children and Youth: Opportunities, Gaps, and Challenges
Bibliographic record
Abstract
Post pandemic increases in mental illness and waitlists for mental health services highlight the urgent need to prevent and mitigate mental health problems in children and youth living in Canada. We describe current dissemination and implementation strategies of evidence-based preventive interventions (EBPIs) for children and youth in Canada that are designed to improve health and well-being. Based on written case studies from 18 Canadian researchers and stakeholders, we examined their approaches to development, dissemination, and implementation of EBPIs. We also summarized the opportunities and challenges faced by these researchers, particularly in sustaining the dissemination and implementing of their evidence-based programs over time. Typically, researchers take responsibility for program dissemination, and they have created a variety of approaches to overcoming costs and challenges. However, despite the availability of many strong, developmentally appropriate EBPIs to support child and youth mental health and well-being, systemic gaps between their development and implementation impede equitable access to and sustainability of these resources.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.147 | 0.277 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.004 | 0.007 |
| 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".