Evidence map›Paper›PMID 38152444›Full record

ArticleDigital health

The portrayal of organ donation on TikTok: A content analysis of popular English-language TikTok videos.

Alessandro R Marcon, Marco Zenone, Timothy Caulfield

Open access · goldAbstract read
In one paragraph

Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
2.0field-weighted citation impact, top 13% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 7 citations in OpenAlex.

  1. Exploring How Rheumatic Fever Is Portrayed on TikTok: A Descriptive Content Analysis.International journal of environmental research and public health · 2025
    Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 1 institution in 1 country.

Alessandro R MarconHealth Law Institute, Faculty of Law, University of Alberta, Edmonton, Alberta, Canada.ORCID https://orcid.org/0000-0001-5018-423X
Marco ZenoneHealth Law Institute, Faculty of Law, University of Alberta, Edmonton, Alberta, Canada.
Timothy CaulfieldHealth Law Institute, Faculty of Law, University of Alberta, Edmonton, Alberta, Canada.
University of Alberta · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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: The finalized videos ( 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.

Indexed as

digital healthdiseasegeneralhealth communicationsmediaorgan donationorgan transplantationPublic healthsocial mediaTikTok

Identifiers

PMID38152444
PMCPMC10752038
OpenAlexW4390234010

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.