Guided parent-delivered cognitive behavioural therapy (GPD-CBT) is an evidence-based, low-burden treatment programme for childhood anxiety disorders with demonstrated efficacy, cost-effectiveness and accessibility. However, it has been tested primarily in Western countries, and the efficacy and cost-effectiveness have not been evaluated in Japanese families. The current study aims to examine GPD-CBT’s efficacy and cost-effectiveness in Japanese samples and explore potential cultural adaptations of the programme.

This study is designed as a Bayesian single-blind randomised controlled trial with two parallel groups: GPD-CBT (intervention group) and a waitlist control group. The primary outcome is remission of primary anxiety disorders evaluated through diagnostic interviews by independent evaluators. Secondary outcomes include child and parent-reported child anxiety symptoms, depressive symptoms and life interference. Additionally, measures of parental psychological characteristics, programme acceptability and quality of life are collected. We will conduct qualitative interviews with parents who participated in the programme and therapists who delivered the intervention to explore potential cultural adaptations. We aim to recruit 54–170 families, depending on the results of sequential Bayesian analyses. GPD-CBT consists of seven weekly 20 min sessions and a 1-month follow-up session. Assessments will be conducted at baseline, 13 weeks post randomisation (primary endpoint for between-group comparison), with an additional 25 weeks post randomisation. The waitlist control group will receive GPD-CBT after the 13-week assessment.

This study has been approved by the Ethics Review Committees of Chiba University and the University of Tokyo. We will disseminate results through academic conference presentations and peer-reviewed journal publications. If the GPD-CBT intervention proves efficacious, we will promote wider implementation in Japan through the development of training programmes for mental health professionals and key stakeholders.

jRCT1032250421 (https://jrct.mhlw.go.jp/latest-detail/jRCT1032250421) and jRCT1030250422 (https://jrct.mhlw.go.jp/latest-detail/jRCT1030250422) registered on 9 October 2025.

Suicide rates have increased over the last couple of decades globally, particularly in the United States and among populations with lower economic status who present at safety-net healthcare systems. Recently, predictive models for suicide risk have shown promise; however, a model for this specific population does not exist.

To develop a predictive risk model of suicide and intentional self-harm (ISH) for patients presenting at the psychiatric emergency department (ED) of JPS Health Network, a safety net medical and mental healthcare system in Texas.

The study used structured and unstructured electronic medical record (EMR) data (2015–2019) and local medical examiner data (2015–2020) to create predictors and outcome variables. All psychiatric ED notes during calendar years 2018 and 2019 were reviewed using natural language processing to identify presentations for any level of self-harm and subsequent manual review of identified visits to accurately classify ED presentations for treatment of an act of intentional self-harm meeting study criteria. Data from 15 987 patients were used to develop and validate a machine learning-based predictive model that leverages rolling window methodology to predict risk repeatedly across a patient’s trajectory. Feature engineering played a prominent role in defining new predictors.

The best model (XGBoost) achieved the area under the receiver operating characteristic curve of 0.81 for 30-day predictions and demonstrated concentration of ISH and suicide attempt events in high-risk quantiles of risk (65% had events in top 0.1% quantile). The predicted risk can be translated into a propensity of events (80% at the highest predicted risk) to facilitate clinical interpretation.

Machine learning-based models can be used with standard EMRs to identify patients presenting at the psychiatric ED with a high risk of ISH and suicide attempts within the next 30 days.

Integration of a predictive model can significantly aid clinical decision-making in safety-net psychiatric EDs.