Seeking Mental Health Support Among College Students in Video-Based Social Media: Content and Statistical Analysis of YouTube Videos

Choi, Bogeum; Kim, Heejun; Huh-Yoo, Jina

doi:10.2196/31944

Cited by 11 publications

(8 citation statements)

References 32 publications

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“…We used inductive content analysis to guide coding of the data. This methodology is appropriate for establishing links between the research objectives and the summary findings, ensuring that these links are transparent [ 39 ] and has been used in studies to identify characteristics of social media videos about college students’ mental health [ 40 ] and to guide the design of digital interventions for mental health management among construction personnel in Nigeria [ 37 ]. Two researchers collaboratively categorized data into known barriers to mental health care in rural populations.…”

Section: Methodsmentioning

confidence: 99%

Self-reliance, Social Norms, and Self-stigma as Barriers to Psychosocial Help-Seeking Among Rural Cancer Survivors With Cancer-Related Distress: Qualitative Interview Study

DeGuzman¹,

Vogel²,

Bernacchi³

et al. 2022

JMIR Form Res

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Background Even when technology allows rural cancer survivors to connect with supportive care providers from a distance, uptake of psychosocial referrals is low. Fewer than one-third of participants in a telemedicine intervention for identifying rural survivors with high distress and connecting them with care accepted psychosocial referral. Objective The purpose of this research was to examine the reasons for which rural cancer survivors did not accept a psychosocial referral. Methods We utilized a qualitative design to address the research purpose. We interviewed participants who had been offered psychosocial referral. Semistructured interviews were conducted 6 weeks later (n=14), and structured interviews were conducted 9 months later (n=6). Data were analyzed descriptively using an inductive approach. Results Ultimately, none of the rural cancer survivors (0/14, 0%) engaged with a psychosocial care provider, including those who had originally accepted referrals (0/4, 0%) for further psychosocial care. When explaining their decisions, survivors minimized their distress, emphasizing their self-reliance and the need to handle distress on their own. They expressed a preference for dealing with distress via informal support networks, which was often limited to close family members. No survivors endorsed public stigma as a barrier to accepting psychosocial help, but several suggested that self-stigma associated with not being able to handle their own distress was a reason for not seeking care. Conclusions Rural cancer survivors’ willingness to accept a psychosocial referral may be mediated by the rural cultural norm of self-reliance and by self-stigma. Interventions to address referral uptake may benefit from further illumination of these relationships as well as a strength-based approach that emphasizes positive aspects of the rural community and individual self-affirmation.

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Section: Methodsmentioning

confidence: 99%

Self-reliance, Social Norms, and Self-stigma as Barriers to Psychosocial Help-Seeking Among Rural Cancer Survivors With Cancer-Related Distress: Qualitative Interview Study

DeGuzman¹,

Vogel²,

Bernacchi³

et al. 2022

JMIR Form Res

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“…For example, individuals with mental health conditions might have sought information and validations for social support and shared experience on YouTube videos. 32 This suggests that YouTube (or other video platforms available on social media) may be a useful tool to improve health literacy and health education for those who need social support. 14 , 15 Furthermore, promoting physical activities on such platforms may be helpful in reducing mood disorder symptoms.…”

Section: Discussionmentioning

confidence: 99%

Association Between Health Information‒Seeking Behavior on YouTube and Physical Activity Among U.S. Adults: Results From Health Information Trends Survey 2020

Lee¹,

Turner²,

Xie³

et al. 2022

AJPM Focus

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“…We then manually grouped the videos based on the video source as previously done in a similar study [ 24 ]. This is because the topic coverage of videos can vary widely depending on the source of the video.…”

Section: Methodsmentioning

confidence: 99%

Understanding the Long Haulers of COVID-19: Mixed Methods Analysis of YouTube Content

Jordan,

Park

2024

JMIR AI

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Background The COVID-19 pandemic had a devastating global impact. In the United States, there were >98 million COVID-19 cases and >1 million resulting deaths. One consequence of COVID-19 infection has been post–COVID-19 condition (PCC). People with this syndrome, colloquially called long haulers, experience symptoms that impact their quality of life. The root cause of PCC and effective treatments remains unknown. Many long haulers have turned to social media for support and guidance. Objective In this study, we sought to gain a better understanding of the long hauler experience by investigating what has been discussed and how information about long haulers is perceived on social media. We specifically investigated the following: (1) the range of symptoms that are discussed, (2) the ways in which information about long haulers is perceived, (3) informational and emotional support that is available to long haulers, and (4) discourse between viewers and creators. We selected YouTube as our data source due to its popularity and wide range of audience. Methods We systematically gathered data from 3 different types of content creators: medical sources, news sources, and long haulers. To computationally understand the video content and viewers’ reactions, we used Biterm, a topic modeling algorithm created specifically for short texts, to analyze snippets of video transcripts and all top-level comments from the comment section. To triangulate our findings about viewers’ reactions, we used the Valence Aware Dictionary and Sentiment Reasoner to conduct sentiment analysis on comments from each type of content creator. We grouped the comments into positive and negative categories and generated topics for these groups using Biterm. We then manually grouped resulting topics into broader themes for the purpose of analysis. Results We organized the resulting topics into 28 themes across all sources. Examples of medical source transcript themes were Explanations in layman’s terms and Biological explanations. Examples of news source transcript themes were Negative experiences and handling the long haul. The 2 long hauler transcript themes were Taking treatments into own hands and Changes to daily life. News sources received a greater share of negative comments. A few themes of these negative comments included Misinformation and disinformation and Issues with the health care system. Similarly, negative long hauler comments were organized into several themes, including Disillusionment with the health care system and Requiring more visibility. In contrast, positive medical source comments captured themes such as Appreciation of helpful content and Exchange of helpful information. In addition to this theme, one positive theme found in long hauler comments was Community building. Conclusions The results of this study could help public health agencies, policy makers, organizations, and health researchers understand symptomatology and experiences related to PCC. They could also help these agencies develop their communication strategy concerning PCC.

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Seeking Mental Health Support Among College Students in Video-Based Social Media: Content and Statistical Analysis of YouTube Videos

Cited by 11 publications

References 32 publications

Self-reliance, Social Norms, and Self-stigma as Barriers to Psychosocial Help-Seeking Among Rural Cancer Survivors With Cancer-Related Distress: Qualitative Interview Study

Self-reliance, Social Norms, and Self-stigma as Barriers to Psychosocial Help-Seeking Among Rural Cancer Survivors With Cancer-Related Distress: Qualitative Interview Study

Association Between Health Information‒Seeking Behavior on YouTube and Physical Activity Among U.S. Adults: Results From Health Information Trends Survey 2020

Understanding the Long Haulers of COVID-19: Mixed Methods Analysis of YouTube Content

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