ArticleJMIR formative research2023
Digital Application of Clinical Staging to Support Stratification in Youth Mental Health Services: Validity and Reliability Study.
Article in JMIR formative research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 13 citations in OpenAlex.
- Emerging Adults' Experiences of Digital Measurement-Based Care in Routine Mental Health Care: Qualitative Study and Conceptual Synthesis.JMIR formative research · 2026Article
- From profiles to precision: incorporating the Psychosocial Assessment Tool (PAT) and the Strengths and Difficulties Questionnaire (SDQ) to inform treatment planning in outpatient psychiatry.European child & adolescent psychiatry · 2026Article
- Advancing Medical Education: Assessing Technology-Enhanced Learning Through the Lens of the Canadian Medical Education Directions for Specialists (CanMEDS) Framework-A Perspective Study.Health science reports · 2025Article
- A Digital Approach for Addressing Suicidal Ideation and Behaviors in Youth Mental Health Services: Observational Study.Journal of medical Internet research · 2024Observational
- Characterization of Psychopathology in Latin American Adolescents Using a Web-Based Screening Tool: Cross-Sectional Study.JMIR formative research · 2024Article
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Authors and funding
13 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAs the demand for youth mental health care continues to rise, managing wait times and reducing treatment delays are key challenges to delivering timely and quality care. Clinical staging is a heuristic model for youth mental health that can stratify care allocation according to individuals' risk of illness progression. The application of staging has been traditionally limited to trained clinicians yet leveraging digital technologies to apply clinical staging could increase the scalability and usability of this model in services.
objectiveThe aim of this study was to validate a digital algorithm to accurately differentiate young people at lower and higher risk of developing mental disorders.
methodsWe conducted a study with a cohort comprising 131 young people, aged between 16 and 25 years, who presented to youth mental health services in Australia between November 2018 and March 2021. Expert psychiatrists independently assigned clinical stages (either stage 1a or stage 1b+), which were then compared to the digital algorithm's allocation based on a multidimensional self-report questionnaire.
resultsOf the 131 participants, the mean age was 20.3 (SD 2.4) years, and 72% (94/131) of them were female. Ninety-one percent of clinical stage ratings were concordant between the digital algorithm and the experts' ratings, with a substantial interrater agreement (κ=0.67; P<.001). The algorithm demonstrated an accuracy of 91% (95% CI 86%-95%; P=.03), a sensitivity of 80%, a specificity of 93%, and an F
conclusionsThis novel digital algorithm is sufficiently robust to be used as an adjunctive decision support tool to stratify care and assist with demand management in youth mental health services. This work could transform care pathways and expedite care allocation for those in the early stages of common anxiety and depressive disorders. Between 11% and 27% of young people seeking care may benefit from low-intensity, self-directed, or brief interventions. Findings from this study suggest the possibility of redirecting clinical capacity to focus on individuals in stage 1b+ for further assessment and intervention.
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