Research and engineering in the quantum domain involve long chains of activity involving theory development, hypothesis formation, experimentation, device prototyping, device testing, and many more. At each stage multiple paths become possible, and of the paths pursued, the majority will lead nowhere. Our quantum metascience approach provides a strategy which enables all stakeholders to gain an overview of those developments along these tracks, that are relevant to their specific concerns. It provides a controlled vocabulary, built out of terms that are (...) designed to be maximally comprehensible to all groups of stakeholders and across all the sub-fields of the quantum domain. (shrink)

Depression is a pervasive and serious mental health concern with far-reaching consequences for individuals. Early detection and intervention are crucial in mitigating its impact. This paper explores the application of machine learning, specifically the random forest algorithm, to analyze social media data for depression detection. Additionally, real-time data collected from students and parents are employed to predict suicidal ideation, making this research a multifaceted approach to addressing mental health issues. Using a random forest algorithm, this study achieved an 86.45% accuracy (...) rate in classifying social media posts as indicative of depression. Furthermore, the research employed an XGBoost algorithm to categorize real-time data into five depression severity stages, achieving an accuracy rate of 83.87%. These results highlight the potential of machine learning in identifying individuals at risk of depression and suicidal ideation. The proposed system offers several advantages, including scalability for analyzing extensive social media datasets, cost-effectiveness, and the ability to provide real-time depression detection, enabling early intervention. Despite challenges associated with noisy and unreliable social media data, as well as the need for models adaptable to diverse populations and contexts, recent advancements in random forest algorithms have shown promise in improving depression detection accuracy. In the future, research in this area should focus on refining machine learning models for more robust and precise depression detection, exploring their applicability across various populations and settings, and developing supportive tools for individuals grappling with depression. (shrink)