Developing an Online Community Advisory Board (CAB) of Parents From Social Media to Co-Design an Human Papillomavirus Vaccine Intervention: Participatory Research Study
Bibliographic record
Abstract
Background: Social media health interventions have grown significantly in recent years. However, researchers are still developing innovative methods to meaningfully engage online communities to inform research activities. Little has been documented describing this approach of using online community advisory boards (CABs) to co-create health communication interventions on social media. Objective: This study describes the formation, engagement, and maintenance of an online CAB focused on co-creating a health education intervention for parents regarding the human papillomavirus (HPV) vaccine. The study provides guiding principles for public health researchers implementing such CABs in future digital health interventions. Methods: In May 2020, Twitter was used to recruit parents of children aged 9-14 years, who were active users of the platform and were interested in serving on a CAB focused on child health and online programs. The recruitment campaign included Twitter (rebranded as X in 2023) advertising tools (eg, "interests" and "audience look-a-likes"). A total of 17 parents completed a screening survey and 6 completed a follow-up phone interview. Following phone interviews, 6 parents were invited to join the CAB, where they committed to a 1-year involvement. The CAB participated in eleven 1-hour online meetings in the first year, contributing to monthly feedback through participatory workbooks. Long-term engagement was sustained through icebreakers and casual online interactions, as well as providing real-time updates to demonstrate CAB feedback integration. An anonymous midterm evaluation was conducted at the end of the project's first year to assess processes and identify future growth opportunities. Results: A total of 6 parents (5 females and 1 male) with children aged 9-14 years from diverse racial and ethnic backgrounds (African American, South Asian American, and White) across 6 states in the United States, representing urban, suburban, and rural areas, agreed to serve as CAB members. All 6 CAB members committed to 1 year of service beginning in July 2020 with 4 extending their participation into a second year (August 2021-August 2022). The CAB provided expert insights and feedback to co-develop the intervention, including character development, narrative content creation, study recruitment, survey development, and intervention delivery. The midterm evaluation showed 100% (6/6) satisfaction among CAB members, who valued the connections with other parents and their contribution to research. While all members felt confident discussing HPV, 83% (5/6) suggested diversifying the group and increasing informal bonding to enhance engagement and inclusivity, especially for differing vaccination views. Conclusions: This study demonstrates that online CABs are a highly effective model for co-creating and informing online health communication interventions. The engagement of parents from diverse backgrounds and the structured use of online tools (eg, interactive workbooks) creates a constructive and thoughtful environment for incorporating parent contributions to research. This study highlights guiding principles to forming, engaging, and maintaining an online CAB to enhance health research and practice.
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 | 0.046 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".