Evaluating the Disease-Related Experiences of TikTok Users With Lupus Erythematosus: Qualitative and Content Analysis
Bibliographic record
Abstract
BACKGROUND: Lupus erythematosus (LE) is an autoimmune condition that is associated with significant detriments to quality of life and daily functioning. TikTok, a popular social networking platform for sharing short videos, provides a unique opportunity to understand experiences with LE within a nonclinical sample, a population that is understudied in LE research. This is the first qualitative study that explores LE experiences using the TikTok platform. OBJECTIVE: This study aims to evaluate the disease-related experiences of TikTok users with LE using qualitative and content analysis. METHODS: TikTok videos were included if the hashtags included #lupus, were downloadable, were in English, and involved the personal experience of an individual with LE. A codebook was developed using a standardized inductive approach of iterative coding until saturation was reached. NVivo (Lumivero), a qualitative analysis software platform, was used to code videos and perform content analysis. Inductive thematic analysis was used to derive themes from the data. RESULTS: A total of 153 TikTok videos met the inclusion criteria. The most common codes were experiences with symptoms (106/153, 69.3%), mucocutaneous symptoms (61/153, 39.9%), and experiences with treatment (59/153, 38.6%). Experiences with symptoms and mucocutaneous symptoms had the greatest cumulative views (25,381,074 and 14,879,109 views, respectively). Five thematic conclusions were derived from the data: (1) mucocutaneous symptoms had profound effects on the mental health and body image of TikTok users with LE; (2) TikTok users' negative experiences with health care workers were often derived from diagnostic delays and perceptions of "medical gaslighting"; (3) TikTok users tended to portray pharmacologic and nonpharmacologic interventions, such as diet and naturopathic remedies, positively, whereas pharmacologic treatments were portrayed negatively or referred to as "chemotherapy"; (4) LE symptoms, particularly musculoskeletal symptoms and fatigue, interfered with users' daily functioning; and (5) although TikTok users frequently had strong support systems, feelings of isolation were often attributed to battling an "invisible illness." CONCLUSIONS: This study demonstrates that social media can provide important, clinically relevant information for health practitioners caring for patients with chronic conditions such as LE. As mucocutaneous symptoms were the predominant drivers of distress in our sample, the treatment of hair loss and rash is vital in this population. However, pharmacologic therapies were often depicted negatively, reinforcing the significance of discussions on the safety and effectiveness of these treatments. In addition, while TikTok users demonstrated robust support systems, feelings of having an "invisible illness" and "medical gaslighting" dominated negative interactions with others. This underscores the importance of providing validation in clinical interactions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".