Mental health interventions for children and young people with long-term health conditions in Children and Young People’s Mental Health Services in England
Bibliographic record
Abstract
BACKGROUND: Almost a quarter of children and young people (CYP) in England have a long-term health condition (LTC), which increases the risk of developing mental health difficulties. There is a lack of understanding regarding the routine provision and efficacy of mental health interventions for CYP with LTCs within Children and Young People's Mental Health Services (CYPMHS). METHODS: This study analysed national service-reported data in England from two secondary datasets. Data were submitted by services between 2011 and 2019. We evaluated data on the presence or absence of a serious physical health or neurological issue, and which interventions were offered. RESULTS: A total of 789 CYP had serious physical health issues and 635 had neurological issues. The most common interventions delivered to CYP in either group have some evidence in the literature. Most CYP showed improvements across a range of outcomes. CONCLUSIONS: This study found that prevalence rates and psychological intervention and outcome data were widely under-reported across both datasets, posing questions about their utility for this population. Such data would benefit from triangulation with data from other sources to understand pathways of care for these young people and the extent to which clinical datasets underreport the number of CYP with LTCs.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".