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Enregistrement W3212033605 · doi:10.1182/blood-2021-153165

Development and Evaluation of a Library of TikToks to Support Recruitment of Committed Hematopoietic Stem Cell Donors from Needed Demographic Groups

2021· article· en· W3212033605 sur OpenAlexaffabout
Brady Park, Lauren Sano, Becky Shields, Sylvia Okonofua, Mikyla Tak, Reihaneh Jamalifar, Aaron Wen, Farnaz Farahbakhsh, Kyla Pires, Kenyon Nisbett, Karen Barboza, Anastasia Pavlenkova, Shirin Pedram, Richard Fattouh, Alexa Gélinas, Bilguissou Bah, Christiane Rochon, Mai T. Duong, Warren Fingrut

Notice bibliographique

RevueBlood · 2021
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueSocial Media in Health Education
Établissements canadiensUniversité de MontréalThe Scarborough HospitalUniversity of British ColumbiaWestern UniversityUniversity of TorontoToronto Metropolitan UniversityBrock UniversityMcMaster UniversityUniversity of AlbertaSimon Fraser UniversityStem Cell NetworkUniversity of Regina
Organismes subventionnairesnon disponible
Mots-clésDonationStakeholderSocial mediaMedicinePublic relationsPolitical scienceWorld Wide WebComputer scienceLaw

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,017
score de la tête « metaresearch » (Gemma)0,031
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,020
Score d'incertitude au seuil0,089

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0170,031
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0020,001
Communication savante0,0040,002
Science ouverte0,0030,004
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0200,004

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,163
Tête enseignante GPT0,362
Écart entre enseignants0,199 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations5
Publié2021
Routes d'admission2
Résumé présentoui

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