Use of Social Media for Health Advocacy for Digital Communities: Descriptive Study
Bibliographic record
Abstract
BACKGROUND: There has been a growth surge in the use of social media among individuals today. The widespread adoption of these platforms, coupled with their engaging features, presents a unique opportunity for the dissemination of health advocacy information. Social media is known as a powerful tool used to share health policy and advocacy efforts and disseminate health information to digital community members and networks. Yet, there is still a gap in the full exploitation of this powerful instrument, among health care professionals, for health advocacy campaigns. OBJECTIVE: This paper aims to describe the process of mobilizing social media platforms such as Twitter (rebranded to X Corp in 2023) for health advocacy of the digital community. Additionally, it aims to share the lessons and insights gained during this digital health advocacy engagement process. METHODS: We performed a comprehensive review of Twitter analytical data to examine the impact of our social media posts. We then consolidated these analytic reports with our meeting logs to describe our systematic, iterative, and collaborative design process to implement social media efforts and generate key lessons learned. RESULTS: Our review of monthly Twitter analytical reports and regular team meeting logs revealed several themes for successful and less successful practices in relation to our social media-based health advocacy efforts. The successful practices noted by the team included using personable, picture-based tweets; using a series of posts on a particular topic rather than an isolated post; leveraging team members' and partners' collaborations in shared posts; incorporating hashtags in tweets; using a balanced mix of texts and graphics in posts; using inclusive (nondestigmatizing) languages in tweeted posts; and use of polls to share tweets. Among the many lessons learned, we also experienced limitations including a lack of comprehensive statistics on Twitter usage for health care-related purposes such as health advocacy and limits in collating the estimates of the actual impact made on the intended digital community members by our posts. CONCLUSIONS: Twitter has been successfully used in promoting health advocacy content, and the social media team aims to explore other social media platforms that have a wider reach than Twitter. We will continue making necessary adjustments in strategies, techniques, and styles to engage the audience as we expand onto new platforms like Instagram and TikTok for health advocacy promotions.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".