Using Social Media to Engage Knowledge Users in Health Research Priority Setting: Scoping Review
Bibliographic record
Abstract
BACKGROUND: The need to include individuals with lived experience (ie, patients, family members, caregivers, researchers, and clinicians) in health research priority setting is becoming increasingly recognized. Social media-based methods represent a means to elicit and prioritize the research interests of such individuals, but there remains sparse methodological guidance on how best to conduct these social media efforts and assess their effectiveness. OBJECTIVE: This review aims to identify social media strategies that enhance participation in priority-setting research, collate metrics assessing the effectiveness of social media campaigns, and summarize the benefits and limitations of social media-based research approaches, as well as recommendations for prospective campaigns. METHODS: We searched PubMed, Embase, Cochrane Library, Scopus, and Web of Science from database inception until September 2021. Two reviewers independently screened all titles and abstracts, as well as full texts for studies that implemented and evaluated social media strategies aimed at engaging knowledge users in research priority setting. We subsequently conducted a thematic analysis to aggregate study data by related codes and themes. RESULTS: A total of 23 papers reporting on 22 unique studies were included. These studies used Facebook, Twitter, Reddit, websites, video-calling platforms, emails, blogs, e-newsletters, and web-based forums to engage with health research stakeholders. Priority-setting engagement strategies included paid platform-based advertisements, email-embedded survey links, and question-and-answer forums. Dissemination techniques for priority-setting surveys included snowball sampling and the circulation of participation opportunities via internal members' and external organizations' social media platforms. Social media campaign effectiveness was directly assessed as number of clicks and impressions on posts, frequency of viewed posts, volume of comments and replies, number of times individuals searched for a campaign page, and number of times a hashtag was used. Campaign effectiveness was indirectly assessed as numbers of priority-setting survey responses and visits to external survey administration sites. Recommendations to enhance engagement included the use of social media group moderators, opportunities for peer-to-peer interaction, and the establishment of a consistent tone and brand. CONCLUSIONS: Social media may increase the speed and reach of priority-setting participation opportunities leading to the development of research agendas informed by patients, family caregivers, clinicians, and researchers. Perceived limitations of the approach include underrepresentation of certain demographic groups and addressing such limitations will enhance the inclusion of diverse research priority opinions in future research agendas.
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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 | 0.071 | 0.258 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.033 | 0.026 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".