National Study of Mental Health and Wellbeing, 2020-2022
The National Study of Mental Health and Wellbeing provides key statistics on mental health issues in Australia, including the prevalence of mental disorders, consultations with health professionals, and the use of mental health-related medications. The study covers a wide range of mental health conditions and offers insights into the impact of mental health on individuals and society.
বিস্তারিত পরিচিতি
The National Study of Mental Health and Wellbeing is a comprehensive survey conducted by the Australian Bureau of Statistics (ABS) to understand the mental health status of the Australian population. Key findings include the prevalence of mental disorders, the most common types of disorders, and the use of mental health services. The study also provides data on the co-occurrence of mental and physical health conditions, consultations with health professionals, and the use of digital technologies for mental health support. This data is crucial for policymakers, researchers, and healthcare providers to develop and implement effective mental health strategies.
আরওডেটাসেট
The Lothian Diary Project - Oct 2020
The Lothian Diary Project consists of 125+ audio/video recordings collected from residents of Edinburgh and the Lothian counties in Scotland. Participants discuss their experiences during different stages of the Covid-19 pandemic. The recordings are accompanied by transcriptions and demographic information.
Mental Health Large Model Lingxin (SoulChat)
Lingxin (SoulChat) is a psychological health large model fine-tuned with millions of Chinese long-text instructions and multi-turn empathetic dialogue data in the field of psychological counseling.
Hugging Face Dataset - NamelessAI Helply
This paper discusses Helply - a synthesized ML training dataset focused on psychology and therapy, created by Alex Scott and published by NamelessAI. The dataset developed by Alex Scott is a comprehensive collection of synthesized data designed to train LLMs in understanding psychological and therapeutic contexts. This dataset aims to simulate real-world interactions between therapists and patients, enabling ML models to learn from a wide range of scenarios and therapeutic techniques.