Exploring the use of social media and online methods to engage knowledge users in creating research agendas: Lessons from a pediatric cancer research priority-setting partnership
Notice bibliographique
Résumé
ABSTRACT While social media is increasingly leveraged to engage knowledge users in research priority-setting, there remains sparse explicit descriptions on how to implement it to build knowledge user-led research agendas. The aim of this study was to review a case study where social media was utilized to engage Canadians within the pediatric cancer community in a research priority-setting exercise; specifically highlighting the social media-based recruitment process, including recommendations on how to optimally engage key potential participants. A priority-setting partnership was launched to develop a stakeholder-driven research agenda in pediatric cancer in Canada. Social –media-based strategies were implemented for participant recruitment, developing a website, launching graphics and advertisements, and engaging internal and external stakeholders. These strategies incorporated the use of various social media platforms. We used descriptive statistics to analyze the data, in addition to the analytics provided by the platforms mentioned. Throughout the duration of the PSP, we garnered 870 Instagram followers, 450 Twitter followers, 69 Facebook page likes, 27 TikTok followers, 20 LinkedIn followers and 789 unique visitors to our website. Our Facebook page reached 28,641 people, while our Instagram profile reached 2,954 people. This social media campaign resulted in 330 individuals completing the initial survey of the PSP, and 197 individuals completing the interim prioritization survey. Social media is a novel approach to engage stakeholders in the development of a research agenda. Our study identified the following strategies as effective in increasing participation in our PSP: (1) creating a unified brand, (2) prioritizing accessibility (e.g., providing alternative text for all images), (3) ensuring social media campaign is reflective of the target audience by diversifying platforms and intermittently tailoring content to specific populations, (4) optimizing campaign’s reach via paid advertisements and circulating promotional material to partner organizations and individuals for them to subsequently share with their networks. AUTHOR SUMMARY Our study evaluated the usage of social media to engage pediatric cancer patients, survivors, their family caregivers, and healthcare providers in setting research priorities for the field of pediatric oncology. As a resource for other research teams, we offer a description of the traction gained by our social media campaign and factors contributing to an increased engagement in the priority-setting process. Social media-based strategies were utilized for participant recruitment; this included developing a website, launching graphics and advertisements, and engaging various stakeholders. Throughout the study duration, we gained 870 Instagram followers, 450 Twitter followers, 69 Facebook page likes, 27 TikTok followers, 20 LinkedIn followers, and 789 unique visitors to the study website. Social media is a relatively new approach to engage individuals for research priority-setting. Our study identified the following strategies as effective methods to increase engagement: (1) creating a unified branding, (2) prioritizing accessibility (e.g., providing alternative text for all images), (3) using various platforms and tailoring content to specific populations, and (4) optimizing campaign reach through paid advertisements and by circulating promotional material to partner organizations and individuals to share within their networks. Further investigation of the privacy implications of social media use for priority-setting research is needed.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,113 | 0,086 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,004 | 0,004 |
| Études des sciences et des technologies | 0,030 | 0,018 |
| Communication savante | 0,019 | 0,011 |
| Science ouverte | 0,005 | 0,022 |
| Intégrité de la recherche | 0,004 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».