Prioritizing core components of successful transitions from child to adult mental health care: a national Delphi survey with youth, caregivers, and health professionals
Bibliographic record
Abstract
Youth accessing mental health care often experience a disruption in care as they attempt to transition between child and adolescent mental health services (CAMHS) and adult mental health services (AMHS). Few studies have evaluated interventions seeking to improve the experience and outcomes of CAMHS-AMHS transitions, in part due to lack of consensus on what constitutes best practices in intervention success. As such, the aim of this study was to engage patients, caregivers, and clinicians to prioritize core components of successful CAMHS-AMHS transitions which can be used in the design or evaluation of transition interventions. As such, a Delphi study was conducted to determine core components of successful CAMHS-AMHS transitions. Guided by the principles of patient-oriented research, three balanced expert panels consisting of youth, caregivers, and clinicians ranked and provided feedback on the importance and feasibility of core components of CAMHS-AMHS transitions. Components endorsed as feasible or important with ≥ 70% agreement from any panel moved to the next round. As a result, a list of 26 core components of CAMHS-AMHS transitions has been refined which can be used in the design, implementation, or evaluation of interventions intended to improve transition experiences and outcomes for youth in mental health care. Youth and families were engaged in an expert advisory role throughout the research process, contributing their important perspectives to the design and implementation of this study, as well as interpretation of the findings.
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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 | 0.032 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".