Investigating #covidnurse Messages on TikTok: Descriptive Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it