Bibliographic record
Abstract
Introduction/Objective: During the COVID-19 pandemic, frontline workers have taken to social media platforms to discuss a variety of issues that concern their personal and professional lives. In particular, TikTok's increased prominence as a social media channel has proved significant for enhancing the public presence of healthcare workers and their ability to disseminate content to a wider audience. The ways that healthcare workers use TikTok draws attention to the type of health information disseminated to the public through social media platforms. This provides the public with succinct and often visually entertaining information that may not be otherwise distributed to them directly from elsewhere. This study also provides relevant insights into how social media-TikTok in particular-can be used as a tool for disseminating knowledge about COVID-19 related topics and combatting misinformation by using the credibility of frontline workers. Methods: This study collected a sample of over 2100 TikTok videos posted by healthcare workers that were coded according to the dominant overarching themes. Results: The themes that arose from this sample were: (1) healthcare workers' mental health and working conditions, (2) healthcare heroes/appreciation, (3) criticism against official authorities, (4) countering misinformation, (5) humor/satire, and (6) educational content. Conclusion: Due to the rise in public appreciation for frontline workers, examining the effects of the pandemic through the eyes of frontline workers has drawn attention to their lived realities in various forms. This study provided some insight into how frontline workers use TikTok to disseminate information and education to the public, often relying on their perceived credibility.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".