From Research to Clinical Practice‐ Youth seeking mental health information online and its impact on the first steps in the patient journey

2021 ◽  
Author(s):  
Jan Scott ◽  
Samuel Hockey ◽  
Laura Ospina Pinillos ◽  
P. Murali Doraiswamy ◽  
Mario Alvarez‐Jimenez ◽  
...  
Keyword(s):  
Mental Health ◽  
Clinical Practice ◽  
Health Information ◽  
Patient Journey ◽  
Mental Health Information

scholarly journals Mental Health, Information and Being Connected: Qualitative Experiences of Social Media Use during the COVID-19 Pandemic from a Trans-National Sample

Healthcare ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 735
Author(s):  
Schoultz Mariyana ◽  
Leung Janni ◽  
Bonsaksen Tore ◽  
Ruffolo Mary ◽  
Thygesen Hilde ◽  
...  
Keyword(s):  
Mental Health ◽  
Social Media ◽  
Health Information ◽  
Online Survey ◽  
Negative Impact ◽  
National Study ◽  
Free Text ◽  
Cross Sectional ◽  
Mental Health Information ◽  
The Uk

Background: Due to the COVID-19 pandemic and the strict national policies regarding social distancing behavior in Europe, America and Australia, people became reliant on social media as a means for gathering information and as a tool for staying connected to family, friends and work. This is the first trans-national study exploring the qualitative experiences and challenges of using social media while in lockdown or shelter-in-place during the current pandemic. Methods: This study was part of a wider cross-sectional online survey conducted in Norway, the UK, USA and Australia during April/May 2020. The manuscript reports on the qualitative free-text component of the study asking about the challenges of social media users during the COVID-19 pandemic in the UK, USA and Australia. A total of 1991 responses were included in the analysis. Thematic analysis was conducted independently by two researchers. Results: Three overarching themes identified were: Emotional/Mental Health, Information and Being Connected. Participants experienced that using social media during the pandemic amplified anxiety, depression, fear, panic, anger, frustration and loneliness. They felt that there was information overload and social media was full of misleading or polarized opinions which were difficult to switch off. Nonetheless, participants also thought that there was an urge for connection and learning, which was positive and stressful at the same time. Conclusion: Using social media while in a shelter-in-place or lockdown could have a negative impact on the emotional and mental health of some of the population. To support policy and practice in strengthening mental health care in the community, social media could be used to deliver practical advice on coping and stress management. Communication with the public should be strengthened by unambiguous and clear messages and clear communication pathways. We should be looking at alternative ways of staying connected.

A Model for the National Mental Health Information Network

2021 ◽  
Author(s):  
Reza Rabiei ◽  
Farkhonde Aasdi ◽  
Hamid Moghaddasi ◽  
Mahdie Shojaei Baghini
Keyword(s):  
Mental Health ◽  
Health Information ◽  
Accurate Information ◽  
Information Network ◽  
Second Phase ◽  
Five Dimensions ◽  
Mental Health Information ◽  
Architectural Model ◽  
National Mental Health ◽  
Data Elements

Aim: Accurate information can be accessed in a timely manner through the Integrated Mental Health Information Network (MHIN). As Iran has no MHIN, this study was undertaken to propose an architectural model.  Method: This research is a sequential mixed method. The organizational structure and database structure of the MHIN was identified, and the architectural model of the NMHIN was presented in two main phases. In the first phase, a quantitative study was conducted in a scoping review with an extensive review of the background, documents, information, and available resources about the mental health information network. In the second phase, to validate the proposed architecture, the Delphi technique was implemented. Questionnaires were distributed and collected both in person and by e-mail, and finally, the data were analyzed using SPSS-19. Results: The model of national MHIN was provided in five dimensions: MH entities, organizational ownership of databases, data elements of each database, linkage among databases, and exchangeable data elements among the databases. Conclusion: This model can be applied as a suitable platform to effectively and efficiently store and use mental health information. So, the available information can be used for providing mental health services more comfortably and appropriately. The results showed that connecting mental health entities can create a flow of information, coordinate MHIN activities, and improve performance, efficiency, and quality of mental health.

scholarly journals A Cross-Cultural Comparison of an Extended Planned Risk Information Seeking Model on Mental Health Among College Students: Cross-Sectional Study

10.2196/15817 ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. e15817 ◽  
Author(s):  
Zhaomeng Niu ◽  
Jessica Fitts Willoughby ◽  
Jing Mei ◽  
Shaochun Li ◽  
Pengwei Hu
Keyword(s):  
Mental Health ◽  
United States ◽  
Cultural Identity ◽  
Health Information ◽  
Information Seeking ◽  
Media Use ◽  
The United States ◽  
Health Information Seeking ◽  
Chinese Sample ◽  
Mental Health Information

Background Approximately 42.5 million adults have been affected by mental illness in the United States in 2013, and 173 million people have been affected by a diagnosable psychiatric disorder in China. An increasing number of people tend to seek health information on the Web, and it is important to understand the factors associated with individuals’ mental health information seeking. Identifying factors associated with mental health information seeking may influence the disease progression of potential patients. Objective This study aimed to test the planned risk information seeking model (PRISM) in China and the United States with a chronic disease, mental illness, and two additional factors, ie, media use and cultural identity, among college students. Methods Data were collected in both countries using the same online survey through a survey management program (Qualtrics). In China, college instructors distributed the survey link among university students, and it was also posted on a leading social media site called Sina Weibo. In the United States, the data were collected in a college-wide survey pool in a large Northwestern university. Results The final sample size was 235 for the Chinese sample and 241 for the US sample. Media use was significantly associated with mental health information–seeking intentions in the Chinese sample (P<.001), and cultural identity was significantly associated with intentions in both samples (China: P=.02; United States: P<.001). The extended PRISM had a better model fit than the original PRISM. Conclusions Cultural identity and media use should be considered when evaluating the process of mental health information seeking or when designing interventions to address mental health information seeking.

scholarly journals Forecasting the Suitability of Online Mental Health Information for Effective Self-Care Developing Machine Learning Classifiers Using Natural Language Features

2021 ◽  
Vol 18 (19) ◽  
pp. 10048
Author(s):  
Meng Ji ◽  
Wenxiu Xie ◽  
Riliu Huang ◽  
Xiaobo Qian
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Health Information ◽  
Self Care ◽  
Health Resources ◽  
Health Issues ◽  
Mental Health Information ◽  
Learning Classifier ◽  
Mental Health Issues ◽  
Bayesian Machine Learning

Background: Online mental health information represents important resources for people living with mental health issues. Suitability of mental health information for effective self-care remains understudied, despite the increasing needs for more actionable mental health resources, especially among young people. Objective: We aimed to develop Bayesian machine learning classifiers as data-based decision aids for the assessment of the actionability of credible mental health information for people with mental health issues and diseases. Methods: We collected and classified creditable online health information on mental health issues into generic mental health (GEN) information and patient-specific (PAS) mental health information. GEN and PAS were both patient-oriented health resources developed by health authorities of mental health and public health promotion. GENs were non-classified online health information without indication of targeted readerships; PASs were developed purposefully for specific populations (young, elderly people, pregnant women, and men) as indicated by their website labels. To ensure the generalisability of our model, we chose to develop a sparse Bayesian machine learning classifier using Relevance Vector Machine (RVM). Results: Using optimisation and normalisation techniques, we developed a best-performing classifier through joint optimisation of natural language features and min-max normalisation of feature frequencies. The AUC (0.957), sensitivity (0.900), and specificity (0.953) of the best model were statistically higher (p < 0.05) than other models using parallel optimisation of structural and semantic features with or without feature normalisation. We subsequently evaluated the diagnostic utility of our model in the clinic by comparing its positive (LR+) and negative likelihood ratios (LR−) and 95% confidence intervals (95% C.I.) as we adjusted the probability thresholds with the range of 0.1 and 0.9. We found that the best pair of LR+ (18.031, 95% C.I.: 10.992, 29.577) and LR− (0.100, 95% C.I.: 0.068, 0.148) was found when the probability threshold was set to 0.45 associated with a sensitivity of 0.905 (95%: 0.867, 0.942) and specificity of 0.950 (95% C.I.: 0.925, 0.975). These statistical properties of our model suggested its applicability in the clinic. Conclusion: Our study found that PAS had significant advantage over GEN mental health information regarding information actionability, engagement, and suitability for specific populations with distinct mental health issues. GEN is more suitable for general mental health information acquisition, whereas PAS can effectively engage patients and provide more effective and needed self-care support. The Bayesian machine learning classifier developed provided automatic tools to support decision making in the clinic to identify more actionable resources, effective to support self-care among different populations.

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