Assessing Community and Social Media Influence to Increase Influenza Vaccine Uptake among Youth in Soweto, South Africa (The Bambisana Study): Protocol for a Mixed Methods Pretest-Posttest Intervention Study
Bibliographic record
Abstract
BACKGROUND: Seasonal influenza has an estimated global reach of 3-5 million infections, with 290,000-650,000 influenza-related deaths yearly. Despite its efficacy in reducing morbidity and mortality, influenza vaccination rates remain low globally and in South Africa. Youth between the ages of 18 and 34 years are not prioritized for influenza vaccines although influenza surveillance in South Africa shows that individuals aged 19-44 years present the highest asymptomatic episodes and the lowest medically attended illness. This creates an opportunity to investigate if and how vaccine demand can be created in the absence of clear imperatives to vaccinate. The study tests the effectiveness of tailored, context-specific education, and community engagement, including community and social media to increase influenza vaccination uptake. Tailored, context-specific education, community engagement, reliable vaccine supply, and free, localized access are all critical for improving perceptions of, increasing confidence in, and motivating the uptake of vaccination. OBJECTIVE: This study will explore strategies to increase influenza vaccine uptake amongst economically marginalized youth aged 18-34 years in Soweto (South-Western Townships), South Africa, where influenza vaccines are not universally accessible through the public health system for this age group. METHODS: The Bambisana Study uses an innovative approach, including community influencers and social media to increase the uptake of influenza vaccines through designing and testing an integrated communications strategy targeted at economically marginalized youth in Soweto, South Africa. The study uses a mixed methods pretest-posttest intervention design to test the effects of the interventions. The intervention will consist of the following components: (1) social media campaign, (2) microinfluencers on and offline, and (3) nonsocial media focused, offline microinfluencer-led engagement within communities. Quantitative data will be collected using a randomized household sample pre- and posttests, and clinic surveys with vaccinees and clinic attendees who declined vaccination. Focus group discussions (FGDs) will be conducted pre- and post intervention with participants aged ≥18 years, and 20 key informant interviews (KIIs) will be conducted with key influencers including religious leaders, traditional healers, and youth leaders. FGDs and KIIs will be audio-recorded and transcribed into English for analysis using framework thematic analysis, and quantitative data analyses will be conducted using SAS Enterprise (Guide 7.15; SAS Institute). RESULTS: This study was funded in December 2022, with recruitment having started in May 2023. As of May 2024, all data collection is complete, with data analyses and preparation of peer-reviewed publications in progress. The first results are expected to be submitted for publication in November 2024. CONCLUSIONS: Enhancing perceptions of, bolstering confidence in, and fostering uptake of vaccination relies heavily on the efficacy of yearly influenza vaccination initiatives, personalized education tailored to specific contexts, active community involvement, consistent vaccine availability, and easily accessible, cost-free distribution channels at the local level. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/60481.
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 | 0.018 | 0.011 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.036 | 0.007 |
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".