mental health
Data were generated from ChatGPT’s responses to 80 counseling questions that college students asked during a school counseling setting. All the responses generated during these simulated counseling sessions were then analyzed using three primary metrics—warmth, empathy, and acceptance—following APA guidelines. The analysis adopted several natural language processing methodologies for emotion detection and empathy measurement to quantify ChatGPT’s high efficacy in presenting the appropriate emotions and reactions for counseling.
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The "Burn Depression Checklist Dataset" is a comprehensive dataset designed to aid in the analysis and understanding of depressive symptoms. The dataset is comprised of 2,600 entries, each corresponding to a unique individual, with 25 features that encapsulate various dimensions of depression, ranging from emotional and psychological symptoms to behavioral patterns.
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779 Views
Data were collected through the Twitter API, focusing on specific vocabulary related to wildfires, hashtags commonly used during the Tubbs Fire, and terms and hashtags related to mental health, well-being, and physical symptoms associated with smoke and wildfire exposure. We focused exclusively on the period from October 8 to October 31, aligning precisely with the duration of the Tubbs Fire. The final dataset available for analysis consists of 90,759 tweets.
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321 Views
A two-stage sampling method was employed for this study. In the first stage, five elderly care institutions in Tangshan Province were randomly selected from a pool of 70 institutions listed in the Notice of Hebei Provincial Department of Civil Affairs on the Grading Results of Elderly Care Institutions in January 2022. In the second stage, cluster sampling was conducted among the elderly residents within these five selected institutions.
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535 Views
The concept of wellness, as proposed by Halbert L. Dunn, recognizes the importance of multiple dimensions, such as social and mental well-being, in maintaining overall health. Neglecting these dimensions can have long-term negative consequences on an individual's mental well-being. In the context of traditional in-person therapy sessions, efforts are made to manually identify underlying factors that contribute to mental disturbances, as these factors, if triggered, can potentially lead to severe mental health disorders.
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159 Views
We provide two datasets extracted from Twitter, in Spanish and English, and annotate each one with approximately 1,500 users who have been diagnosed with one of nine different mental disorders (ADHD, Autism, Anxiety, Bipolar, Depression, Eating disoders, OCD, PTSD and Schizophrenia) along with 1,700 matched-control users.
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7885 Views
This dataset maps mood to information about the events that influenced the mood. The dataset was obtained using a web-based data collection interface developed by us. The dataset consists of 5245 days of data from 134 participants in the experiment.
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750 Views