Investigating #covidnurse Messages on TikTok: Descriptive Study
Bibliographic record
Abstract
BACKGROUND: During a time of high stress and decreased social interaction, nurses have turned to social media platforms like TikTok as an outlet for expression, entertainment, and communication. OBJECTIVE: The purpose of this cross-sectional content analysis study is to describe the content of videos with the hashtag #covidnurse on TikTok, which included 100 videos in the English language. METHODS: At the time of the study, this hashtag had 116.9 million views. Each video was coded for content-related to what nurses encountered and were feeling during the COVID-19 pandemic. RESULTS: Combined, the 100 videos sampled received 47,056,700 views; 76,856 comments; and 5,996,676 likes. There were 4 content categories that appeared in a majority (>50) of the videos: 83 showed the individual as a nurse, 72 showed the individual in professional attire, 58 mentioned/suggested stress, 55 used music, and 53 mentioned/suggested frustration. Those that mentioned stress and those that mentioned frustration received less than 50% of the total views (n=21,726,800, 46.17% and n=16,326,300, 34.69%, respectively). Although not a majority, 49 of the 100 videos mentioned the importance of nursing. These videos garnered 37.41% (n=17,606,000) of the total views, 34.82% (n=26,759) of the total comments, and 23.85% (n=1,430,213) of the total likes. So, despite nearly half of the total videos mentioning how important nurses are, these videos received less than half of the total views, comments, and likes. CONCLUSIONS: Social media and increasingly video-related online messaging such as TikTok are important platforms for social networking, social support, entertainment, and education on diverse topics, including health in general and COVID-19 specifically. This presents an opportunity for future research to assess the utility of the TikTok platform for meaningful engagement and health communication on important public health issues.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".