How are large-scale One Health initiatives targeting infectious diseases and antimicrobial resistance evaluated? A scoping review
Bibliographic record
Abstract
While One Health initiatives are gaining in popularity, it is unclear if and how they are evaluated when implementation at scale is intended. The main purpose of this scoping review was to describe how One Health initiatives targeting infectious diseases and antimicrobial resistance at a large scale are evaluated. Secondary objectives included identifying the main facilitators and barriers to the implementation and success of these initiatives, and how their impacts were assessed. Twenty-three studies evaluating One Health initiatives were eligible. Most studies included the human (n = 22) and animal (n = 15) sectors; only four included the environment sector. The types of evaluated initiative (non-exclusive) included governance (n = 5), knowledge (n = 6), protection (n = 17), promotion (n = 16), prevention (n = 9), care (n = 8), advocacy (n = 10) and capacity (n = 10). Studies used normative (n = 4) and evaluative (n = 20) approaches to assess the One Health initiatives, the latter including impact (n = 19), implementation (n = 8), and performance (n = 7) analyses. Structural and economic, social, political, communication and coordination-related factors, as well as ontological factors, were identified as both facilitators and barriers for successful One Health initiatives. These results identified a wide range of evaluation methods and indicators used to demonstrate One Health's added values, strengths, and limitations: the inherent complexity of the One Health approach leads to the use of multiple types of evaluation. The strengths and remaining gaps in the evaluation of such initiative highlight the relevance of comprehensive, mixed-method, context-sensitive evaluation frameworks to inform and support the implementation of One Health initiatives by stakeholders in different governance settings.
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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 | 0.092 | 0.272 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.027 | 0.025 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".