No abstract

Children were able to provide potentially useful opinions of CAMHS. In a time of limited resources it is imperative that the voices of children and their parents are acknowledged in order to improve accessibility and experiences within CAMHS.

Purpose – Mental health of children and young people is often discussed in terms of mental illness, however, such an approach is limited. The purpose of this paper is to explore young people's views of what mental health is and how to stay mentally healthy. Design/methodology/approach – The paper investigated young people's views on these two issues through a series of workshops. In total 218, 13-year-old schoolchildren produced posters with their impressions of the issues. Themes that young people identified were then discussed with them in terms of the existing Bright Futures definition of mental health. Poster responses were subsequently transcribed and thematically analysed. Findings – The paper identified a number of themes for each question. Mental health was viewed in terms of personal attributes of an individual, illness, ability for personal management and establishing social relations. Young people saw mental health maintained through a combination of lifestyle choices, personal attributes, management of self and environment, social support and relationships, as well as treatment of illness. These themes corresponded to the ones identified by the Bright Futures. Research limitations/implications – This study highlights the complexity of young people's views on the meaning of mental health. They were also more positive, open and competent in discussing mental health than previously suggested. However, a more systematic investigation of views and attitudes is necessary, including younger children. Additionally, health care professionals are likely to benefit from young people's engagement in planning and implementing strategies for better mental health. Originality/value – This paper is one of the few to investigate the positive meaning of mental health with young people.

Background Common mental disorders can be effectively treated with psychotherapy, but some patients do not respond well and require timely identification to prevent treatment failure. We aimed to develop and validate a dynamic model to predict psychological treatment outcomes, and to compare the model with currently used methods, including expected treatment response models and machine learning models. MethodsIn this prediction model development and validation study, we obtained data from two UK studies including patients who had accessed therapy via Improving Access to Psychological Therapies (IAPT) services managed by ten UK National Health Service (NHS) Trusts between March, 2012, and June, 2018, to predict treatment outcomes. In study 1, we used data on patient-reported depression (Patient Health Questionnaire 9 [PHQ-9]) and anxiety (Generalised Anxiety Disorder 7 [GAD-7]) symptom measures obtained on a session-by-session basis (Leeds Community Healthcare NHS Trust dataset; n=2317) to train the Oracle dynamic prediction model using iterative logistic regression analysis. The outcome of interest was reliable and clinically significant improvement in depression (PHQ-9) and anxiety (GAD-7) symptoms. The predictive accuracy of the model was assessed in an external test sample (Cumbria Northumberland Tyne and Wear NHS Foundation Trust dataset; n=2036) using the area under the curve (AUC), positive predictive values (PPVs), and negative predictive values (NPVs). In study 2, we retrained the Oracle algorithm using a multiservice sample (South West Yorkshire Partnership NHS Foundation Trust, North East London NHS Foundation Trust, Cheshire and Wirral Partnership NHS Foundation Trust, and Cambridgeshire and Peterborough NHS Foundation Trust; n=42 992) and compared its performance with an expected treatment response model and five machine learning models (Bayesian updating algorithm, elastic net regularisation, extreme gradient boosting, support vector machine, and neural networks based on a multilayer perceptron algorithm) in an external test sample (Whittington Health NHS Trust; Barnet Enfield and Haringey Mental Health Trust; Pennine Care NHS Foundation Trust; and Humber NHS Foundation Trust; n=30 026). Findings The Oracle algorithm trained using iterative logistic regressions generalised well to external test samples, explaining up to 47•3% of variability in treatment outcomes. Prediction accuracy was modest at session one (AUC 0•59 [95% CI 0•55-0•62], PPV 0•63, NPV 0•61), but improved over time, reaching high prediction accuracy (AUC 0•81 [0•77-0•86], PPV 0•79, NPV 0•69) as early as session seven. The performance of the Oracle model was similar to complex (eg, including patient profiling variables) and computationally intensive machine learning models (eg, neural networks based on a multilayer perceptron algorithm, extreme gradient boosting). Furthermore, the predictive accuracy of a more simple dynamic algorithm including only baseline and index-session scores was comparable to more complex algorithms that includ...

This is a repository copy of A systematic review and meta-analysis of the good-enough level (GEL) literature.