Early identification of childhood mental health disorders is a critical public health objective. Existing screening approaches, largely dependent on observer reports, are resource-intensive and may overlook subtle internalized symptoms. The analysis of children’s linguistic expression presents a scalable and potentially more objective alternative. This study evaluates whether combining natural language processing (NLP) of children’s essays with conventional risk factors improves the detection of mental health difficulties in school-age populations, relative to models based on a single data source. We conducted a prospective analysis using data from the UK-based National Child Development Study (NCDS), a national birth cohort initiated in 1958. Data from birth, age 7, and age 11 assessments were analyzed. The final sample included 8,981 children (4,428 [49.3

Background: Mental health disorders affect one in five young people, yet most do not receive timely identification or support. AI has been proposed as a means of enabling earlier identification, but its clinical readiness remains uncertain. We assessed whether externally validated AI tools can fulfil this promise for CYP. Methods: We conducted a systematic review of MEDLINE, Embase, PsycINFO, Scopus, Web of Science, ACM Digital Library, and IEEE Xplore from Jan 1, 2014, to Dec 31, 2024. We included peer-reviewed studies using ML to predict or identify DSM-5 mental health disorders in individuals aged 0-25 years that underwent external validation in independent datasets. Nine reviewers independently screened records, extracted data in accordance with TRIPOD-AI, and assessed risk of bias using PROBAST-AI. The primary outcome was diagnostic accuracy on external validation. We undertook meta-analysis where sufficient homogeneity was present. This study is registered with PROSPERO, CRD42024628355. Findings: Of 25,003 records screened, 39 studies met the inclusion criteria. Research was concentrated on ASD (n=23), ADHD (n=7), suicidality (n=6), and depression (n=4); psychotic disorders were absent. Mean external validation AUC was 0.776 (95% CI 0.683-0.870) for ASD, 0.788 (95% CI 0.679-0.927) for ADHD, 0.844 (95% CI 0.793-0.894) for suicidality, and 0.708 (95% CI 0.670-0.783) for depression. Meta-analysis of MRI-based ASD studies (n=6) showed a pooled external sensitivity of 0.832 (95% CI 0.708-0.955) and specificity of 0.716 (95% CI 0.582-0.849), with high heterogeneity (I2 >75%). Most studies (74%) identified existing diagnoses rather than predicting future onset. Risk of bias was high in 51% of studies. Only one AI tool had reached clinical deployment. Interpretation: Current AI tools primarily classify existing diagnoses rather than predict emerging risk, limiting their value for early identification. The emerging field of AI-based early identification of youth mental health problems requires reorientation towards longitudinal, pragmatic data sources and implementation-ready approaches. We propose a methodological checklist for developing new AI algorithms in young people's mental health to help bridge the gap between algorithmic performance and clinical impact.

BACKGROUND:Early exposure to risk and adversity is a potent predictor of mental health difficulties. Though risks vary by gender, little attention was paid towards the associations both within risks and of risks across genders. OBJECTIVE:We sought to identify networks of a wider range of risks (experiences and behaviors that might threaten the person's wellbeing and safety before the age of 18 years). And we aimed to have a better understanding of the specific risk configurations across genders and to develop potential clinical interventions. PARTICIPANTS AND SETTING:This study explores network structures of early risks among 45,210 children and adolescents (aged 5 to 18) from longitudinal data in the UK. METHODS:Network analysis was applied to investigate the associations among risks and to identify the central risks across genders. RESULTS:Stable connections across genders in different assessments of risks (e.g., risks of self-harm and suicide). Risks related to violence could be core risks in all networks. Some gender differences in the context of early risks are also identified. For example, substance misuse and exhibiting violent or offending behavior are more closely associated among the male children that took the Brief Risk Assessment. CONCLUSIONS:Gendered associations between risks could be of value for both intervention and prevention. More attention should be paid to risks related to violence in clinical practice and policy making. Future study could record risks more precisely, utilize data from multiple time points and take more social-demographic factors into consideration to obtain integrated and comprehensive results.

Background: Early detection of childhood mental health disorders remains challenging due to gaps in current screening approaches that lack sensitivity to subtle psychological indicators and rely heavily on observable behaviors. We investigated whether integrating machine learning with natural language processing of children's written expressions could enhance early detection of potential mental disorders among school-age children. Methods: This prospective birth cohort study used National Child Development Study (NCDS) data, analyzing 8,981 children born in 1958 in the United Kingdom. Mental health outcomes were assessed using the Bristol Social Adjustment Guide (BSAG) and Rutter A Scale at age 11, with cases defined by scores above 95th and 90th percentiles. Predictive models combined traditional risk factors with natural language features extracted from children's essays describing their imagined future at age 25. We developed eight machine learning models using various predictor combinations, evaluating performance through receiver operating characteristic (ROC) values. Results: Using BSAG 95th percentile threshold, models combining top five selected variables with essay features achieved significantly higher predictive capability (ROC:0.77, 95%CI:0.71-0.83) compared to models using all variables (ROC:0.70, 95%CI:0.63-0.76) or essay features alone (ROC:0.67, 95%CI:0.60-0.74). At 90th percentile threshold, this integrated approach showed similar improvement (ROC:0.81, 95%CI:0.78-0.85). Key predictors included gestational length, maternal parity, parental age, residential characteristics, parental engagement metrics, and children's BMI. Sensitivity analyses using Rutter A Scale confirmed these findings. Conclusion: Combining machine learning with natural language processing of children's future-oriented essays offers a promising approach for early detection of childhood mental health disorders. This integrated screening method could facilitate more timely intervention, though validation in contemporary populations is needed before clinical implementation.

BACKGROUND:Rates of childhood mental health problems are increasing in the UK. Early identification of childhood mental health problems is challenging but critical to children's future psychosocial development. This is particularly important for children with social care contact because earlier identification can facilitate earlier intervention. Clinical prediction tools could improve these early intervention efforts. AIMS:Characterise a novel cohort consisting of children in social care and develop effective machine learning models for prediction of childhood mental health problems. METHOD:We used linked, de-identified data from the Secure Anonymised Information Linkage Databank to create a cohort of 26 820 children in Wales, UK, receiving social care services. Integrating health, social care and education data, we developed several machine learning models aimed at predicting childhood mental health problems. We assessed the performance, interpretability and fairness of these models. RESULTS:Risk factors strongly associated with childhood mental health problems included age, substance misuse and being a looked after child. The best-performing model, a gradient boosting classifier, achieved an area under the receiver operating characteristic curve of 0.75 (95% CI 0.73-0.78). Assessments of algorithmic fairness showed potential biases within these models. CONCLUSIONS:Machine learning performance on this prediction task was promising. Predictive performance in social care settings can be bolstered by linking diverse routinely collected data-sets, making available a range of heterogenous risk factors relating to clinical, social and environmental exposures.

AIMS:Developing integrated mental health services focused on the needs of children and young people is a key policy goal in England. The THRIVE Framework and its implementation programme, i-THRIVE, are widely used in England. This study examines experiences of staff using i-THRIVE, estimates its effectiveness, and assesses how local system working relationships influence programme success. METHODS:This evaluation uses a quasi-experimental design (10 implementation and 10 comparison sites.) Measurements included staff surveys and assessment of 'THRIVE-like' features of each site. Additional site-level characteristics were collected from health system reports. The effect of i-THRIVE was evaluated using a four-group propensity-score-weighted difference-in-differences model; the moderating effect of system working relationships was evaluated with a difference-in-difference-in-differences model. RESULTS:Implementation site staff were more likely to report using THRIVE and more knowledgeable of THRIVE principles than comparison site staff. The mean improvement of fidelity scores among i-THRIVE sites was 16.7, and 8.8 among comparison sites; the weighted model did not find a statistically significant difference. However, results show that strong working relationships in the local system significantly enhance the effectiveness of i-THRIVE. Sites with highly effective working relationships showed a notable improvement in 'THRIVE-like' features, with an average increase of 16.41 points (95% confidence interval: 1.69-31.13, P-value: 0.031) over comparison sites. Sites with ineffective working relationships did not benefit from i-THRIVE (-2.76, 95% confidence interval: - 18.25-12.73, P-value: 0.708). CONCLUSIONS:The findings underscore the importance of working relationship effectiveness in the successful adoption and implementation of multi-agency health policies like i-THRIVE.

Objectives:To create a theoretical framework of mental health risk factors to inform the development of prediction models for young people's mental health problems. Materials and Methods:We created an initial prototype theoretical framework using a rapid literature search and stakeholder discussion. A snowball sampling approach identified experts for the Delphi study. Round 1 sought consensus on the overall approach, framework domains, and life course stages. Round 2 aimed to establish the points in the life course where exposure to specific risk factors would be most influential. Round 3 ranked risk factors within domains by their predictive importance for young people's mental health problems. Results:The final framework reached consensus after 3 rounds and included 287 risk factors across 8 domains and 5 life course stages. Twenty-five experts completed round 3. Domains ranked as most important were "Social and Environmental" and "Psychological and Mental Health." Ranked lists of risk factors within domains and heat maps showing the salience of risk factors across life course stages were generated. Discussion:The study integrated multidisciplinary expert perspectives and prioritized health equity throughout the framework's development. The ranked risk factor lists and life stage heat maps support the targeted inclusion of risk factors across developmental stages in prediction models. Conclusion:This theoretical framework provides a roadmap of important risk factors for inclusion in early identification models to enhance the predictive accuracy of childhood mental health problems. It offers a useful theoretical reference point to support model building for those without domain expertise.

What Is Known on the Subject? Mental health care can be delivered remotely through video and telephone consultations. Remote consultations may be cheaper and more efficient than in person consultations.What the Paper Adds to Existing Knowledge Accessing community mental health care through remote consultations is perceived as not possible or beneficial for all service users. Delivering remote consultations may not be practical or appropriate for all clinicians or community mental health teams.What are the Implications for Practice? Remote consultation cannot be a 'one-size-fits-all' model of community mental health care. A flexible approach is needed to offering remote consultation that considers its suitability for the service-user, service and clinician.IntroductionResponding to COVID-19, community mental health teams in the UK NHS abruptly adopted remote consultations. Whilst they have demonstrable effectiveness, efficiency, and economic benefits, questions remain around the acceptability, feasibility and medicolegal implications of delivering community mental health care remotely.AimTo explore perceived advantages, challenges, and practice adaptations of delivering community mental health care remotely.MethodsTen community mental health teams in an NHS trust participated in a service evaluation about remote consultation. Fifty team discussions about remote consultation were recorded April-December 2020. Data analysis used a framework approach with themes being coded within a matrix.ResultsThree major horizontal themes of operations and team functioning, clinical pathways, and impact on staff were generated, with vertical themes of advantages, challenges, equity and adaptations.DiscussionRemote consultation is an attractive model of community mental healthcare. Clinical staff note benefits at individual (staff and service-user), team, and service levels. However, it is not perceived as a universally beneficial or practical approach, and there are concerns relating to access equality.Implications for PracticeThe suitability of remote consultation needs to be considered for each service-user, clinical population and clinical role. This requires a flexible and hybrid approach, attuned to safeguarding equality.

AIMS:Identifying children and/or adolescents who are at highest risk for developing chronic depression is of utmost importance, so that we can develop more effective and targeted interventions to attenuate the risk trajectory of depression. To address this, the objective of this study was to identify young people with persistent depressive symptoms across adolescence and young adulthood and examine the prospective associations between factors and persistent depressive symptoms in young people. METHODS:We used data from 6711 participants in the Avon Longitudinal Study of Parents and Children. Depressive symptoms were assessed at 12.5, 13.5, 16, 17.5, 21 and 22 years with the Short Mood and Feelings Questionnaire, and we further examined the influence of multiple biological, psychological and social factors in explaining chronic depressive symptoms. RESULTS:Using latent class growth analysis, we identified four trajectories of depressive symptoms: persistent high, persistent low, persistent moderate and increasing high. After applying several logistic regression models, we found that loneliness and feeling less connected at school were the most relevant factors for chronic course of depressive symptoms. CONCLUSIONS:Our findings contribute with the identification of those children who are at highest risk for developing chronic depressive symptoms.

Background Epidemiological research may require linkage of information from multiple organizations. This can bring two problems: (1) the information governance desirability of linkage without sharing direct identifiers, and (2) a requirement to link databases without a common person-unique identifier. Methods We develop a Bayesian matching technique to solve both. We provide an open-source software implementation capable of de-identified probabilistic matching despite discrepancies, via fuzzy representations and complete mismatches, plus de-identified deterministic matching if required. We validate the technique by testing linkage between multiple medical records systems in a UK National Health Service Trust, examining the effects of decision thresholds on linkage accuracy. We report demographic factors associated with correct linkage. Results The system supports dates of birth (DOBs), forenames, surnames, three-state gender, and UK postcodes. Fuzzy representations are supported for all except gender, and there is support for additional transformations, such as accent misrepresentation, variation for multi-part surnames, and name re-ordering. Calculated log odds predicted a proband’s presence in the sample database with an area under the receiver operating curve of 0.997–0.999 for non-self database comparisons. Log odds were converted to a decision via a consideration threshold θ and a leader advantage threshold δ . Defaults were chosen to penalize misidentification 20-fold versus linkage failure. By default, complete DOB mismatches were disallowed for computational efficiency. At these settings, for non-self database comparisons, the mean probability of a proband being correctly declared to be in the sample was 0.965 (range 0.931–0.994), and the misidentification rate was 0.00249 (range 0.00123–0.00429). Correct linkage was positively associated with male gender, Black or mixed ethnicity, and the presence of diagnostic codes for severe mental illnesses or other mental disorders, and negatively associated with birth year, unknown ethnicity, residential area deprivation, and presence of a pseudopostcode (e.g. indicating homelessness). Accuracy rates would be improved further if person-unique identifiers were also used, as supported by the software. Our two largest databases were linked in 44 min via an interpreted programming language. Conclusions Fully de-identified matching with high accuracy is feasible without a person-unique identifier and appropriate software is freely available.

The Goldacre review, published in April, 2022,1 is a landmark evaluation of the use, availability, and safety of National Health Service (NHS) data across all four nations of the UK. The review underscores the necessary role of data in driving health-care improvement and innovation, and the potential risks inherent in using data routinely contributed by health service users. The review recommends a radical overhaul in NHS data curation, access, and analysis, and, crucially, argues that substantial new resources must be marshalled to make this aspiration a reality.

Reviews into universal interventions to improve help seeking in young people focus on specific concepts, such as behaviour, do not differentiate between interpersonal and intrapersonal help seeking, and often report on statistical significance, rather than effect size. The aim of this review was to address the gaps highlighted above, to investigate the impact of universal, school-based interventions on help-seeking in children and young people, as well as to explore longer term impact. Four databases were searched. Data were extracted on country of origin, design, participant, school, and intervention characteristics, the help-seeking concept measured (e.g. knowledge, attitude/intention, behaviour), the duration between baseline and each follow-up (if applicable) and effect sizes at each follow-up. Quality assessment of the studies was undertaken using the Effective Public Health Practice Project (EPHPP) quality assessment tool. Overall, 14 different interventions met inclusion criteria. The majority of the studies were rated low in the quality assessment. Three constructs were most frequently reported a) intrapersonal attitudes towards help-seeking, b) interpersonal attitudes towards help-seeking and c) intrapersonal intended help-seeking. Findings around intervention effect were mixed. There was tentative evidence that interventions impacting interpersonal attitudes produced small effect sizes when measured between 3 and 6 months post intervention and that when effect sizes were initially observed intrapersonal attitudes, this remained at 3–6 month follow-up. Further work should pay attention to implementation factors, understanding the core ingredients needed to deliver effective interventions and whether embedding mental health education could help sustain or top up effect sizes from help-seeking interventions.

The National i-THRIVE Programme seeks to evaluate the impact of the NHS England-funded whole system transformation on child and adolescent mental health services (CAMHS). This article reports on the design for a model of implementation that has been applied in CAMHS across over 70 areas in England using the 'THRIVE' needs-based principles of care. The implementation protocol in which this model, 'i-THRIVE' (implementing-THRIVE), will be used to evaluate the effectiveness of the THRIVE intervention is reported, together with the evaluation protocol for the process of implementation. To evaluate the effectiveness of i-THRIVE to improve care for children and young people's mental health, a cohort study design will be conducted. N = 10 CAMHS sites that adopt the i-THRIVE model from the start of the NHS England-funded CAMHS transformation will be compared to N = 10 'comparator sites' that choose to use different transformation approaches within the same timeframe. Sites will be matched on population size, urbanicity, funding, level of deprivation and expected prevalence of mental health care needs. To evaluate the process of implementation, a mixed-methods approach will be conducted to explore the moderating effects of context, fidelity, dose, pathway structure and reach on clinical and service level outcomes. This study addresses a unique opportunity to inform the ongoing national transformation of CAMHS with evidence about a popular new model for delivering children and young people's mental health care, as well as a new implementation approach to support whole system transformation. If the outcomes reflect benefit from i-THRIVE, this study has the potential to guide significant improvements in CAMHS by providing a more integrated, needs-led service model that increases access and involvement of patients with services and in the care they receive.

BACKGROUND:This scoping review aimed to overview studies that used administrative data linkage in the context of child maltreatment to improve our understanding of the value that data linkage may confer for policy, practice, and research. METHODS:We searched MEDLINE, Embase, PsycINFO, CINAHL, and ERIC electronic databases in June 2019 and May 2020 for studies that linked two or more datasets (at least one of which was administrative in nature) to study child maltreatment. We report findings with numerical and narrative summary. RESULTS:We included 121 studies, mainly from the United States or Australia and published in the past decade. Data came primarily from social services and health sectors, and linkage processes and data quality were often not described in sufficient detail to align with current reporting guidelines. Most studies were descriptive in nature and research questions addressed fell under eight themes: descriptive epidemiology, risk factors, outcomes, intergenerational transmission, predictive modelling, intervention/service evaluation, multi-sector involvement, and methodological considerations/advancements. CONCLUSIONS:Included studies demonstrated the wide variety of ways in which data linkage can contribute to the public health response to child maltreatment. However, how research using linked data can be translated into effective service development and monitoring, or targeting of interventions, is underexplored in terms of privacy protection, ethics and governance, data quality, and evidence of effectiveness.

There are increasing rates of internalising difficulties, particularly anxiety and depression, being reported in children and young people in England. School-based universal prevention programmes are thought to be one way of helping tackle such difficulties. This paper describes an update to a four-arm cluster randomised controlled trial ( http://www.isrctn.com/ISRCTN16386254 ), investigating the effectiveness of three different interventions when compared to usual provision, in English primary and secondary pupils. Due to the COVID-19 pandemic, the trial was put on hold and subsequently prolonged. Data collection will now run until 2024. The key changes to the trial outlined here include clarification of the inclusion and exclusion criteria, an amended timeline reflecting changes to the recruitment period of the trial due to the COVID-19 pandemic and clarification of the data that will be included in the statistical analysis, since the second wave of the trial was disrupted due to COVID-19. Trial registration ISRCTN Registry ISRCTN16386254. Registered on 30 August 2018.

Despite an increasing focus on schools to deliver support and education around mental health and wellbeing, interventions are often not sustained beyond initial funding and research. In this review, the barriers and facilitators to sustaining mental health and wellbeing interventions in schools are explored. A systematic review was conducted using keywords based on the terms: 'sustainability', 'school', 'intervention', 'mental health', and 'emotional wellbeing'. Six online databases (PsycINFO, Embase, MEDLINE, British Education Index, ERIC, and Web of Science) and relevant websites were searched resulting in 6160 unique references. After screening, 10 articles were included in the review and extracted data were qualitatively synthesized using thematic analysis. Data synthesis led to the identification of four sustainability factors at the school level (school leadership, staff engagement, intervention characteristics, and resources) and one at the wider system level (external support). These factors were separated into 15 themes and discussed as barriers and facilitators to sustainability (for example, school culture and staff turnover). Most articles included no definition of sustainability, and nearly all barriers and facilitators were discussed at the school level. The findings suggest that more longitudinal and theory-driven research is required to develop a clearer picture of the sustainability process.

The substantial time that children and young people spend in schools makes them important sites to trial and embed prevention and early intervention programmes. However, schools are complex settings, and it can be difficult to maintain school engagement in research trials; many projects experience high levels of attrition. This commentary presents learning from two large-scale, mixed-methods mental health intervention trials in English schools. The paper explores the barriers and challenges to engaging schools in promotion or early intervention research and offers detailed recommendations for other researchers.

Resources and activities offered by Voluntary, Community and Social Enterprise (VCSE) organisations could play a key role in supporting communities with their mental health. Whilst policy makers have become increasingly interested in using such asset-based approaches to improve mental health and well-being, the sustainability of these approaches remains underresearched. In this review, we explored the factors affecting the sustainability of community mental health assets. We conducted a systematic review of the literature using keywords based on three key terms: 'sustainability', 'mental health issues' and 'service provision'. Our search strategy was deployed in four electronic databases (MEDLINE, Web of Science, ASSIA and IBSS) and relevant websites were also searched. The literature search was conducted in November and December 2020 and yielded 2486 results. After title and abstract screening, 544 articles were subjected to full-text review. A total of 16 studies were included in a narrative synthesis. Studies included a broad range of community interventions and 30 factors affecting sustainability were identified across three sustainability levels: micro (individual), meso (organisational) and macro (local/national/global). Factors were discussed as barriers or facilitators to sustainability. A key barrier across all sustainability levels was funding (cost to individual participants, lack of available funding for VCSEs, economic uncertainty) whilst a key facilitator was connectedness (social connections, partnering with other organisations, linking with national public health systems). Nearly all articles included no definition of sustainability and the majority of factors identified here were at the meso/organisational level. As funding was found to be such a prevalent barrier, more research into macro level factors (e.g. government policies) is required.

Background Improving data access, sharing, and linkage across local authorities and other agencies can contribute to improvements in population health. Whilst progress is being made to achieve linkage and integration of health and social care data, issues still exist in creating such a system. As part of wider work to create the Cambridge Child Health Informatics and Linked Data (Cam-CHILD) database, we wanted to examine barriers to the access, linkage, and use of local authority data. Methods A systematic literature search was conducted of scientific databases and the grey literature. Any publications reporting original research related to barriers or enablers of data linkage of or with local authority data in the United Kingdom were included. Barriers relating to the following issues were extracted from each paper: funding, fragmentation, legal and ethical frameworks, cultural issues, geographical boundaries, technical capability, capacity, data quality, security, and patient and public trust. Results Twenty eight articles were identified for inclusion in this review. Issues relating to technical capacity and data quality were cited most often. This was followed by those relating to legal and ethical frameworks. Issue relating to public and patient trust were cited the least, however, there is considerable overlap between this topic and issues relating to legal and ethical frameworks. Conclusions This rapid review is the first step to an in-depth exploration of the barriers to data access, linkage and use; a better understanding of which can aid in creating and implementing effective solutions. These barriers are not novel although they pose specific challenges in the context of local authority data.

The Covid-19 crisis necessitated rapid adoption of remote consultations across National Health Service (NHS) child and adolescent mental health services (CAMHS). This study aimed to understand practitioners' experiences of rapid implementation of remote consultations across CAMHS in one NHS trust in the east of England. Data were collected through a brief questionnaire documenting clinicians' experiences following remote delivery of services. The questionnaire began before 'lockdown' and focused on assessment consultations (n = 102) as part of a planned move to virtual assessment. As the roll-out of remote consultations was extended at lockdown, we extended the questionnaire to include all remote clinical contacts (n = 202). Despite high levels of initial concern, clinicians' reports were positive overall; importantly, however, their experiences varied by team. When restrictions on face-to-face working are lifted, a blended approach of remote and face-to-face service delivery is recommended to optimise access and capacity while retaining effective and safe care.