Development of a risk factor framework to inform machine learning prediction of young people's mental health problems: a Delphi study

构建风险因素框架以指导机器学习预测青少年心理健康问题:一项德尔菲研究

阅读:2

Abstract

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.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。