Exploring How Youth Use TikTok for Mental Health Information in British Columbia: Semistructured Interview Study With Youth (Preprint)
Bibliographic record
Abstract
<sec> <title>BACKGROUND</title> TikTok (ByteDance) experienced a surge in popularity during the COVID-19 pandemic as a way for people to interact with others, share experiences and thoughts related to the pandemic, and cope with ongoing mental health challenges. However, few studies have explored how youth use TikTok to learn about mental health. </sec> <sec> <title>OBJECTIVE</title> This study aims to understand how youth used TikTok during the COVID-19 pandemic to learn about mental health and mental health support. </sec> <sec> <title>METHODS</title> Semistructured interviews were conducted with 21 youths (aged 12-24 years) living in British Columbia, Canada, who had accessed TikTok for mental health information during the COVID-19 pandemic. Interviews were audio-recorded, transcribed verbatim, coded, and analyzed using an inductive, data-driven approach. </sec> <sec> <title>RESULTS</title> A total of 3 overarching themes were identified describing youth’s experiences. The first theme centered on how TikTok gave youth easy access to mental health information and support, which was particularly helpful during the COVID-19 pandemic to curb the effects of social isolation and the additional challenges of accessing mental health services. The second theme described how the platform provided youth with connection, as it gave youth a safe space to talk about mental health and allowed them to feel seen by others going through similar experiences. This helped normalize and destigmatize conversations about mental health and brought awareness to various mental health conditions. Finally, the last theme focused on how this information led to action, such as trying different coping strategies, discussing mental health with peers and family, accessing mental health services, and advocating for themselves during medical appointments. Across the 3 themes, youth expressed having to be mindful of bias and misinformation, highlighting the barriers to identifying and reporting misinformation and providing individualized advice on the platform. </sec> <sec> <title>CONCLUSIONS</title> Findings suggest that TikTok can be a useful tool to increase mental health awareness, reduce stigma, and encourage youth to learn and address their mental health challenges while providing a source of peer connection and support. Simultaneously, TikTok can adversely impact mental health through repetitive exposure to mentally distressing content and misleading diagnosis and treatment information. Regulations against harmful content are needed to mitigate these risks and make TikTok safer for youth. Efforts should also be made to increase media and health literacy among youth so that they can better assess the information they consume online. </sec>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".