A social media intervention for communicating vaccine safety in low- and middle-income countries: protocol for a pilot study
Bibliographic record
Abstract
Vaccine safety is a concern that continues to drive hesitancy and refusal in populations in low-and-middle income countries (LMICs). Communicating about vaccine safety is a strategy that can successfully change personal and community perceptions and behaviors toward vaccination. The COVID-19 infodemic emergency with the rapid rollout of new vaccines and new technology, demonstrated the need for good and effective vaccine safety communication. The Vaccine Safety Net (VSN), a WHO-led global network of websites that provide reliable information on vaccine safety offers the ideal environment for gathering web and social media analytics for measuring impact of vaccine safety messages. Its members work with a wide range of populations, in different geographic locations and at many levels including national, regional, and local. We propose to undertake a pilot study to evaluate the feasibility of implementing COVID-19 vaccine safety communications with VSN members working in LMICs and to assess the impact of communications on public knowledge, attitudes, and perceptions.
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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 | 0.016 | 0.017 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.084 | 0.015 |
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".