Policies to prevent zoonotic spillover: protocol for a systematic scoping review of evaluative evidence
Bibliographic record
Abstract
INTRODUCTION: The increasing incidence of pathogen transmission from animals to humans (zoonotic spillover events) has been attributed to behavioural practices and ecological and socioeconomic change. As these events sometimes involve pathogens with epidemic or pandemic potential, they pose a serious threat to population health. Public policies may play a key role in preventing these events. The aim of this review is to identify evaluations of public policies that target the determinants of zoonotic spillover, examining approaches taken to evaluation, choice of outcomes measures and evidence of effectiveness. Our approach to identifying and analysing this literature will be informed by a One Health lens, acknowledging the interconnectedness of human, animal and environmental health. METHODS AND ANALYSIS: A systematic scoping review methodology will be used. To identify articles, we will search Medline, SCOPUS, Web of Science and Global Health in May 2021 using search terms combining animal health and the animal-human interface, public policy, prevention and zoonoses. We will screen titles and abstracts and extract data according to published guidelines for scoping reviews. All evaluations of public policies aiming to prevent zoonotic spillover events will be eligible for inclusion. We will summarise key data from each study, mapping policies along the spillover pathway and outlining the range of policies, approaches to evaluation and outcome measures. Review findings will provide a useful reference for researchers and practitioners, outlining the state of the evaluative evidence around policies to prevent zoonotic spillover. ETHICS AND DISSEMINATION: Formal ethical approval is not required, because the study does not involve primary data collection. The findings of this study will be disseminated through a peer-reviewed publication, presentations and summaries for key stakeholders.
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 | 0.164 | 0.168 |
| Meta-epidemiology (narrow) | 0.008 | 0.007 |
| Meta-epidemiology (broad) | 0.020 | 0.019 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.078 | 0.019 |
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".