Exploring Grassroots Indicators for Pandemic Prevention, Preparedness, and Response: A Systematic Narrative Review
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has revealed how conventional top-down, expert-driven indicators often fail to align with local community realities, marginalising their perspectives, concerns, knowledge, and narratives. However, the limitations of pandemic-related and global health security indicators are not unique but reflect recurring patterns across major social metrics. In response, an alternative paradigm advocates for grassroots-inclusive approaches to developing indicators. Our objective is to assess how and why grassroots-inclusive approaches complement top-down approaches to developing indicators, and to synthesise their theoretical and practical contributions to public health. METHODS: We conducted a scoping review in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. We systematically searched six databases (MEDLINE, Embase, CINAHL, Web of Science, Scopus, and PsycINFO), as well as Google Scholar, to identify relevant articles published from their inception to September 1, 2024. We included peer-reviewed articles, opinion pieces, and book chapters, narratively synthesising their findings. RESULTS: This review included 43 studies from various disciplines. Across these studies, communities co-produced indicators through participatory workshops, interviews, and consensus exercises in areas such as environmental sustainability, disaster resilience, public health, well-being, and local development. The reported strengths included greater local relevance, community ownership, and accountability, alongside challenges in sustaining participation, integrating into top-down systems, and addressing data gaps. Notably, no study applied grassroots-inclusive indicators to health security or pandemic preparedness. CONCLUSION: Despite retrieving and analysing articles from various disciplines, no study has specifically applied grassroots-inclusive indicators to health security or pandemic preparedness. However, the evidence clearly shows that it is both feasible and practical to integrate expert and non-expert perspectives when developing indicators.
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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 | 0.048 | 0.152 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.021 | 0.019 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".