Longitudinal Youth in Transition Study (LYiTS): protocol for a multicentre prospective cohort study of youth transitioning out of child and adolescent mental health services at age 18
Bibliographic record
Abstract
INTRODUCTION: Transition between health services is widely recognised as a problematic hurdle. Yet, the factors necessary for successful transition out of child and adolescent mental health services (CAMHS) as youth reach the service boundary at age 18 are poorly understood. Further, fragmentation and variability among the services provided by mental health organisations serve to exacerbate mental illness and create unnecessary challenges for youth and their families. The primary aim of the Longitudinal Youth in Transition Study (LYiTS) is to describe and model changes in psychiatric symptoms, functioning and health service utilisation at the transition out of CAMHS at age 18 and to identify key elements of the transition process that are amendable to interventions aimed at ensuring continuity of care. METHODS AND ANALYSIS: A prospective longitudinal cohort study will be conducted to examine the association between psychiatric symptoms, functioning and mental health and health service use of youth aged 16-18 as they transition out of child mental health services at age 18. We will recruit a sample of (n=350) participants from child and adolescent psychiatric programmes at two hospital and two community mental health sites and conduct assessments annually for 3 years using standardised measures of psychiatric symptoms, functioning and health service utilisation. ETHICS AND DISSEMINATION: Ethics approval has been obtained at all four recruitment sites. We will disseminate the results through conferences, open access publications and webinars.
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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 | 0.028 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.037 | 0.008 |
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".