The portrayal of organ donation on TikTok: A content analysis of popular English-language TikTok videos
Bibliographic record
Abstract
Objective: TikTok is one of the most popular social media platforms and plays a role in shaping public perceptions. This research examined how organ donation was portrayed on the platform. Methods: We built a dataset of the most popular English-language TikTok videos that used the hashtags #organdonor or #organdonation. We then performed content analysis on the 400 most viewed videos after limiting data set inclusion to one video per user account. Results: = 313) had generated nearly 80 million views and 10 million likes. Featuring both donors (56.2%) and recipients (44.1%), videos shared experiences that celebrated and lamented lost donor lives (41.8%) while also celebrating transplantation successes (31.3%). Very few videos included public solicitation (2.9%). Common video traits included detailing medical procedures (45.4%), presenting honor walks (10.9%), and displaying donors and recipients connecting or wanting to connect (16.9%). Videos mostly had a positive (74.1%) versus negative (10.2%) leaning tone. Conclusion: Far from superficially glamorizing organ donation/transplantation processes and procedures, popular English-language TikTok videos depicted what we perceived as highly emotional and expository experiences. While the videos likely offered learning and cathartic opportunities for individuals and communities, they also highlight some tensions between personal anecdotes and data/research. Findings from this research can inform public outreach efforts as well as policies related to protecting anonymity and celebrating donors with honor walks. Indeed, given TikTok's increasing popularity and influence, it could be a valuable tool to meaningfully learn from, and engage with, patient and donor communities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".