Examining TikTok’s Potential for Community-Engaged Digital Knowledge Mobilization With Equity-Seeking Groups
Bibliographic record
Abstract
Social media is increasingly being leveraged by researchers to engage in public debates and rapidly disseminate research results to health care providers, health care users, policy makers, educators, and the general public. This paper contributes to the growing literature on the use of social media for digital knowledge mobilization, drawing particular attention to TikTok and its unique potential for collaborative knowledge mobilization with underserved communities who experience barriers to health care and health inequities (eg, equity-seeking groups). Setting the TikTok platform apart from other social media are the unique audiovisual video editing tools, together with an impactful algorithm, that make knowledge dissemination and exchange with large global audiences possible. As an example, we will discuss digital knowledge mobilization with trans and nonbinary (trans) communities, a population that experiences barriers to health care and is engaged in significant peer-to-peer health information sharing on the web. To demonstrate, analytics data from 13 selected TikTok videos on the topic of research on gender-affirming medicine (eg, hormonal therapy and surgeries) are presented to illustrate how knowledge is disseminated within the trans community via TikTok. Considerations for researchers planning to use TikTok for digital knowledge mobilization and other related community engagement with equity-seeking groups are also discussed. These include the limitations of TikTok analytics data for measuring knowledge mobilization, population-specific concerns related to community safety on social media, the spread of disinformation, barriers to internet access, and commercialization and intellectual property issues. This paper concludes that TikTok is an innovative social media platform that presents possibilities for achieving transformative, community-engaged knowledge mobilization among researchers, underserved health care users, and their health care providers, all of whom are necessary to achieve better health care and population health outcomes.
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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 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".