Social Media Told Me I Have A Mental Illness
2024
- 349Usage
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Usage349
- Downloads249
- Abstract Views100
Project Description
Objective: This study assessed and addressed the attitudes and behaviors towards mental health information on social media, specifically regarding how it influences viewers behaviors and attitudes relating to mental health. Method: Using a quantitative research design, the study collected data through Qualtrics from a 15 question survey called the Mental Health Information Scale (MHI). The survey, distributed across several social media platforms, aimed to understand user attitudes and engagement with mental health content into three dimensions: stigma, self-diagnosis, and confidence in the validity of the content. Findings: There were 99 participants in this study, 69.9% female, 21.4% male. The factor analysis includes 5 underlying dimensions found within the (MHI) scale. The factor analysis demonstrated that the survey questions had low reliability. The regression analysis highlighted that men and older participants are less likely to self-diagnose themselves and have more skepticism of the mental health content they see on social media. The results of this study highlight a need for specific interventions such as digital literacy and policy changes to address the effects of viewing mental health related content online.
Bibliographic Details
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