Community Engagement Within the Evaluation of Public Policies for Zoonotic Spillover Prevention: A Secondary Matrix Analysis
Bibliographic record
Abstract
Despite the overall health, economic, and social costs of zoonotic spillover, its impacts are not felt equally around the globe. Engaging local communities in primary spillover prevention may help to better ensure equity is considered in research and policy-making activities. Our study aimed to gain an understanding of how and at what level community engagement (CE) has been incorporated into the evaluation of public policies for zoonotic spillover prevention. We conducted a secondary analysis on an existing dataset from a systematic review, beginning with a structured deductive content analysis. A secondary matrix of analysis was engaged using an adapted CE tool for screening the included articles based on their level of CE. We then characterized relevant themes based on the CE elements within the included articles. Of the 95 articles included, 55 had no level of CE reported. Among the included articles that had some level of CE, elements included the platforming of community consultation, community training for involvement in spillover prevention research, cultural and language considerations being engaged, community protection and awareness programmes for public health and biosecurity, and community-centered data collection processes being engaged. Our findings highlight the persistent equity gaps in appropriately engaging affected communities within the evaluation of public policies for spillover prevention.
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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 | 0.109 | 0.277 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.019 | 0.022 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".