Development and Evaluation of a Library of TikToks to Support Recruitment of Committed Hematopoietic Stem Cell Donors from Needed Demographic Groups
Bibliographic record
Abstract
Abstract Introduction TikTok is a rapidly growing social media platform that allows users to develop and share short videos. We hypothesized that a library of videos developed through TikTok (TikToks) would support the recruitment of committed hematopoietic stem cell donors from needed demographic groups (i.e. young, male, from diverse ancestries). Methods Members of the community of practice (CoP) in stem cell donor recruitment in Canada (facebook.com/groups/stemcellclub) were activated to develop TikToks. Training was provided during e-meetings of the CoP (8/2020, 1/2021, 5/2021) and in a video published online (stemcellclub.ca/training), highlighting the principles of effective TikTok design. These principles included the use of engaging hooks, music, and calls to action; short duration (< 30s); high educational value; and appeal to diverse donors. The training also outlined how to: record content, adjust clip lengths, apply audiovisual effects, and share across social media platforms. A CoP TikTok committee was formed to develop and review TikToks prior to publication. Following launch, we evaluated stakeholder perspective on these TikToks and the impact 1) across social and traditional media and 2) on eligible donors' knowledge and attitudes towards donation. Results Between 9/2020-7/2021, a network of TikTok channels was launched by CoP members, including a national donor recruitment TikTok library (tiktok.com/@stemcellclub). A total of 217 TikToks were produced across these channels (median length 17s, range 4-52s), covering a range of educational topics, designed for use in specific recruitment campaigns, and featuring unique video effects (Fig. A). The TikToks accumulated over 234,000 Views, 42,000 Likes, 3,000 Comments, and 14,200 Shares on TikTok, were republished by Canadian media outlets (e.g. CBC [twitter.com/cbcnewsbc/status/1361511367426080773], CTV News [ctvnews.ca/health/meet-the-women-hoping-to-recruit-more-stem-cells-donors-from-black-communities-1.5314038, ctvnews.ca/health/pride-month-tiktok-drive-encourages-stem-cell-donations-from-gay-bi-men-1.5475113], Victoria News [vicnews.com/news/most-black-canadians-wont-find-a-stem-cell-donor-in-time-this-group-is-working-to-change-that]) and were highlighted by major medical organizations (e.g. Canadian Blood Services [blood.ca/en/stories/meet-stem-cell-club, blood.ca/en/stories/stem-cell-club-volunteers-aim-save-lives-pride-month-campaign], American Association of Blood Banks [aabb.org/news-resources/news/article/2021/02/01/twitter-tiktok-aabb-virtual-journal-club-assesses-use-of-multimedia-resources-for-donor-recruitment]). 33 CoP members from 6 provinces across Canada, with a median of 2 years of recruitment experience, completed a post-launch survey. The majority felt that TikToks promote donation in an attention-grabbing way (94%), engage younger donors (100%), and teach key points in a short time period (94%). The majority were confident in their ability to make TikToks (63%), but felt they would benefit from additional training (63%). 46 eligible stem cell donors (from 12 different non-Caucasian ancestral groups; living in 5 provinces across Canada) completed surveys evaluating the impact of TikToks on their knowledge and attitudes towards donation. No participants were registered as donors and only four had a personal connection to an individual who needed a stem cell transplant. After being shown a series of TikToks, mean scores on a 6-question stem cell donation knowledge test improved from 59% to 73% (p=0.0012) (Fig. B); mean scores on a modified Simmons Ambivalence Scale decreased from 52% to 30% (p<0.0001) (Fig. C); and participants were more willing to register as donors (70% vs. 39%, p=0.0011). Participants reported that viewing TikToks positively impacted on their decision to register (87%), helped them understand stem cell donation (89%), and would help them talk about stem cell donation with friends/family (78%). Conclusions We report the first published experience using TikToks in a donor recruitment context. Our TikToks achieved significant social and traditional impact in a short period of time, and supported recruitment of committed stem cell donors from needed demographic groups. Our work is relevant to recruitment organizations worldwide seeking to modernize their recruitment approaches. Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.
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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 | 0.017 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".