Scale-up of Global Child and Youth Mental Health Services: A Scoping Review
Bibliographic record
Abstract
Numerous influential policy and scientific bodies are calling for more rapid advances in the scale-up of child and youth mental health services (CYMHS). A number of CYMHS innovations hold promise for advancing scale-up but little is known about how real-world efforts are progressing. We conducted a scoping review to identify promising approaches to CYMHS scale-up across the globe. Searches were completed in six databases (Academic Search Complete, CINAHL, MEDLINE, PsychInfo, PubMed, and Web of Science). Article selection and synthesis were conducted in accordance to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Reviews (PRISMA-ScR) checklist. A second search focused on low-and-middle-income countries (LMIC) was conducted based on the Cochrane Library recommended search filters of the World Bank listed LMIC countries. Authors used a double coding strategy during the title/abstract and full-text review. Twenty-eight articles meeting the eligibility criteria were identified that described 22 initiatives (in 11 different countries). Our review found the majority of published scale-up studies in CYMHS were not informed by scale-up frameworks in design or reporting. The methods and outcomes used in the identified articles were highly variable and limited our ability to draw conclusions about comparative effectiveness although promising approaches emerged. Successes and failures identified in our review largely reflect consensus in the broader literature regarding the need for strategies to better navigate the complexities of system and policy implementation while ensuring CYMHS interventions fit local contexts.
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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 | 0.073 | 0.195 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.032 | 0.030 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".