The communication of mental health is an important branch of health communication, and it is also an important factor affecting people's physical and mental health. With the increasing pressure of life, people's mental health problems have huge challenges. Under the enormous pressure of economy and life, people's mental health problems are becoming increasingly prominent. This calls for attention to mental health issues. In the context of new media, knowledge about mental health can be disseminated through the Internet and mobile platforms. This approach will spread awareness of mental health prevention and basic issues. Mental health problems are also a manifestation of the lack of humanistic spirit. Excellent works related to humanistic spirit can promote the relief of mental health problems. Literature can contribute to the development of mental health problems. This research studies the communication of mental health issues in the context of new media using literary works as a carrier. At the same time, it also considers big data-related algorithms to mine the traditional characteristics of mental health problems. The research results show that new media technology can well assist the dissemination of mental health education, and literary works also contribute to the dissemination of mental health education knowledge. Collaborative filtering algorithm and atrous convolution algorithm can better predict the relevant characteristics in the process of mental health communication. For the algorithm, its maximum similarity index reached 0.987 when recommending mental health propagation paths using new media technology. For ACNN, the smallest prediction error is only 1.78%.
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http://dx.doi.org/10.3389/fpsyg.2022.997558 | DOI Listing |
J Med Internet Res
January 2025
Department of Clinical Psychology and Psychotherapy, Institute of Psychology and Education, Ulm University, Ulm, Germany.
Background: Unobtrusively collected objective sensor data from everyday devices like smartphones provide a novel paradigm to infer mental health symptoms. This process, called smart sensing, allows a fine-grained assessment of various features (eg, time spent at home based on the GPS sensor). Based on its prevalence and impact, depression is a promising target for smart sensing.
View Article and Find Full Text PDFJ Prim Care Community Health
January 2025
Instituto de Investigación Biomédica de Málaga, Málaga, Spain.
Aim: To investigate the detection and initial management of first psychotic episodes, as well as established schizophrenia, within the primary care of the Andalusian Health System.
Background: Delay in detecting and treating psychosis is associated with slower recovery, higher relapse risk, and poorer long-term outcomes. Often, psychotic episodes go unnoticed for years before a diagnosis is established.
Personal Disord
January 2025
Laboratoire sur les Interactions Cognition, Action, Émotion (LICAE), UFR STAPS, Universite Paris-Nanterre.
This study aimed to assess measurement invariance for the Five-Factor Inventory for (Oltmanns & Widiger, 2020) across nine national samples from four continents ( = 6,342), and to validate a French translation in seven French-speaking national samples. All were convenience samples of adults. Exploratory factor analyses supported a four-factor structure in the French-speaking Western samples (Belgium, Canada, France, and Switzerland) while a three-factor structure was preferred in the French-speaking African samples (Burkina Faso and Togo), and no adequate structure was found in the Indian sample.
View Article and Find Full Text PDFPersonal Disord
January 2025
Department of Psychological Science, Kent State University.
Antagonism is a personality domain located in most major trait models and is central to multiple personality disorders. This construct has been linked to many societally harmful externalizing behaviors (e.g.
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