Evidence needs and community involvement in policy decisions for vaccine-preventable diarrhoeal infections among children under the age of five years: Stakeholder engagement in Ethiopia, Kenya, and Malawi
Bibliographic record
Abstract
Abstract Diarrhoeal diseases are a leading cause of morbidity and mortality among children under five years in low- and middle-income countries (LMICs) with Shigella and Enterotoxigenic Escherichia coli (ETEC), both targets of ongoing vaccine development, playing major roles. To inform the introduction of vaccines against these pathogens in Ethiopia, Kenya, and Malawi, we assessed evidence needs for technical policy decisions and community engagement in national policy development processes. Using in-depth interviews (IDIs) with 27 institutional stakeholders (health ministries, researchers, and partners) and focus group discussions (FGDs) with community representatives (one per country), we collected data from May 2023 to May 2024 and undertook thematic analysis. Institutional stakeholders emphasised the need for robust, localised evidence on disease burden, vaccine efficacy, safety, cost-effectiveness, programmatic feasibility, alongside structured mechanisms to integrate evidence into policy. They noted the absence of diarrhoeal disease-specific technical working groups in their countries and advocated for their establishment. Meanwhile, community representatives expressed vaccine acceptance but stressed the importance of clear communication of information delivered by trusted health workers and leaders. They highlighted the need for comprehensive information on new vaccines, particularly regarding their safety and potential side effects. While community involvement in national policy-making processes was deemed low, participants highlighted the value of meaningful engagement to ensure ownership and uptake. The findings highlight the importance of robust evidence generation, effective communication, and inclusive community engagement to facilitate the successful introduction of Shigella and ETEC vaccines. Addressing these gaps can accelerate adoption, improve readiness, and foster trust in vaccination programmes.
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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 | 0.049 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".