Implementing Social Media Strategies in Community-Partnered HIV Research: Practical Considerations From 3 Ongoing Studies
Bibliographic record
Abstract
BACKGROUND: In recent years, social media has emerged as a pivotal tool in implementation science efforts to address the HIV epidemic. Engaging community partners is essential to ensure the successful and equitable implementation of social media strategies. There is a notable lack of scholarship addressing the operational considerations for studies using social media strategies in community-partnered HIV research. This article seeks to bridge this gap by consolidating field notes and practical considerations derived from 3 ongoing NIH-supported studies focused on Ending the HIV Epidemic in the United States. OBJECTIVE: This article aims to inform the design, planning, and implementation of operationally effective community-partnered social media strategies in HIV research, ultimately contributing to enhancements in HIV practice and improved outcomes across the HIV prevention and care continua. METHODS: Supported by the University of California, Los Angeles Rapid, Rigorous, Relevant (3R) Implementation Science Hub, the 3 Ending the HIV Epidemic projects convened to form the community-partnered social media campaigns working group. The working group used the Consolidated Framework for Implementation Research to help identify and organize key barriers and facilitators of relevance to implementation of the projects' social media strategies. Given the high degree of interrelatedness across reported factors, the working group thematically synthesized the content into 5 practical considerations to inform use of community-partnered social media strategies in HIV research. RESULTS: The practical considerations identified by the community-partnered social media campaigns working group include the following: (1) the power and pitfalls of social media platforms (ie, opportunities and challenges inherent to social media platforms that may affect use of social media strategies in HIV research), (2) messengers and messages matter (ie, ensuring the appropriateness, acceptability, and quality of social media messengers and content), (3) the significance of the sociopolitical environment (ie, characterizing the sociopolitical environment surrounding HIV and its potential impact on implementing social media strategies to reach priority populations), (4) investing in academic-community partnerships (ie, cultivating positive and productive academic-community partnerships to support implementation of social media strategies in HIV research), and (5) the alignment of the institutional environment and research approach (ie, assessing and working to address features of institutional environments that may impact implementation of social media strategies in community-partnered HIV research). CONCLUSIONS: As use of social media in HIV research and practice continues to grow, the practical considerations presented in this paper can help research teams anticipate factors that may impact implementation of community-partnered social media strategies and take early action to mitigate potential challenges. By understanding and addressing the unique challenges and opportunities of social media in community-partnered HIV research, we can leverage these platforms to accelerate progress toward ending the HIV epidemic.
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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 | 0.645 | 0.437 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.017 | 0.024 |
| Scholarly communication | 0.032 | 0.034 |
| Open science | 0.014 | 0.035 |
| Research integrity | 0.021 | 0.020 |
| Insufficient payload (model declined to judge) | 0.006 | 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".