Exploring the use of social media and online methods to engage persons with lived experience and healthcare professionals in creating research agendas: Lessons from a pediatric cancer research priority-setting partnership
Bibliographic record
Abstract
Social media is increasingly used to engage persons with lived experience and healthcare professionals in research, however, there remains sparse guidance on how to effectively use social media to engage these groups in research agenda-setting. Here we report our process and experience utilizing a social media campaign to engage Canadians within the pediatric cancer community in a research priority-setting exercise. Following the James Lind Alliance method, we launched a priority-setting partnership (PSP) to develop a child with cancer-, survivor-, family member-, and healthcare professional-based Canadian pediatric cancer research agenda. Social media-based strategies were implemented to recruit participants for two PSP surveys, including preparatory activities, developing a website, launching graphics and advertisements, and engaging internal and external networks. Descriptive statistics of our data and analytics provided by the platforms are used presently to report our process. The framework we implemented involved preparing for social media use, identifying a target audience, developing campaign content, conducting the campaign, refining the campaign as needed, and evaluating its success. Our process resulted in a substantial social media-based reach, good survey completion rates, and a successfully developed pediatric cancer community-specified research agenda. Social media may represent a useful approach to engage persons with lived experience and healthcare professionals in research agenda development. Based on our experience, we present strategies to increase social media campaign engagement that may be useful to those seeking to conduct health research priority-setting exercises.
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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 | 0.102 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.028 | 0.018 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.004 | 0.025 |
| Research integrity | 0.003 | 0.007 |
| 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".