Scoping review of stepped care interventions for mental health and substance use service delivery to youth and young adults
Bibliographic record
Abstract
AIMS: Many young people with mental health and/or substance use concerns do not have access to timely, appropriate, and effective services. Within this context, stepped care models (SCMs) have emerged as a guiding framework for care delivery, inspiring service innovations across the globe. However, substantial gaps remain in the evidence for SCMs as a strategy to address the current systemic challenges in delivering services for young people. This scoping review aims to identify where these gaps in evidence exist, and the next steps for addressing them. METHODS: A scoping review was conducted involving both peer-reviewed and grey literature. Eligible studies explored SCMs implemented in the various health care settings accessed by young people aged 12-24 seeking treatment for mental health and substance use challenges. After screening titles and abstracts, two reviewers examined full-text articles and extracted data to create a descriptive summary of the models. RESULTS: Of the 656 studies that were retrieved, 51 studies were included and grouped by study team for a final yield of 43 studies. Almost half of the studies were focused on the adult population (i.e., 18 and over), and most did not specify interventions for young people. Among the SCMs, substantial variability was found in almost every aspect of the models. CONCLUSIONS: Considering the current body of evidence, there is an urgent need for a consensus position on the definition, implementation, and outcome measures required for rigorously assessing the utility of SCMs for young people.
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 | 0.052 | 0.179 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.022 | 0.023 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".