Identifying core components and indicators of successful transitions from child to adult mental health services: a scoping review
Bibliographic record
Abstract
The aim of this scoping review was to identify the core components of interventions that facilitate successful transition from child and adolescent mental health services to adult mental health services. In the absence of rigorous evaluations of transition program effectiveness for transitioning youth with mental health care needs, these core components can contribute to informed decisions about promising program and intervention strategies. This review examined data from 87 peer-reviewed and non-academic documents to determine the characteristics that support the transition process and to identify opportunities for system and program improvement. Data were extracted and synthesized using a descriptive analytic framework. A major finding of this review is a significant lack of measurable indicators in the academic and gray literature. This review did identify 26 core components organized within the framework of the six core elements of healthcare transitions. Policy makers, practitioners, and administrators can use the core components to guide decisions about transition program and intervention content. Confirmation of the impact of these core program components on youth outcomes awaits the conduct of rigorous randomized trials. Future research also needs to explicitly focus on the development of indicators to evaluate transition programs and interventions.
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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 | 0.019 | 0.077 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.024 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".