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
Bibliographic record
Abstract
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.
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 | 0.113 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.030 | 0.018 |
| Scholarly communication | 0.019 | 0.011 |
| Open science | 0.005 | 0.022 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".