Characterizing how One Health is defined and used within primary research: A scoping review
Bibliographic record
Abstract
Background and Aim: One Health (OH) approach can be used in multiple ways to tackle a wide range of complex problems, making OH research applications and definitions difficult to summarize. To improve our ability to describe OH research applications, we aimed to characterize (1) the terms used in OH definitions within primary research articles reporting the use of the OH approach, and (2) the who, what, where, when, why, and how (5Ws and H) of the OH primary research articles. Materials and Methods: A scoping review was conducted using nine databases and the search term “One Health” in June 2021. Articles were screened by two reviewers using pre-specified eligibility criteria. The search yielded 11,441 results and screening identified 252 eligible primary research articles. One Health definitions and 5Ws and H data were extracted from these studies. Results: Definitions: One Health was labeled as an “approach” (n = 79) or “concept” (n = 30) that is “multi/cross/inter/trans-disciplinary” (n = 77), “collaborative” (n = 54), “interconnected” (n = 35), applied “locally/regionally/nationally/globally” (n = 84), and includes health pillars (“human” = 124, “animal” = 122, “environmental/ecosystem” = 118). WHEN: Article publication dates began in 2010 and approximately half were published since 2020 (130/252). WHERE: First authors most often had European (n = 101) and North American (n = 70) affiliations, but data collection location was more evenly distributed around the world. WHO: The most common disciplines represented in affiliations were human health/biology (n = 198), animal health/biology (n = 157), food/agriculture (n = 81), and environment/geography (n = 80). WHAT: Infectious disease was the only research topic addressed until 2014 and continued to be the most published overall (n = 171). Antimicrobial resistance was the second most researched area (n = 47) and the diversity of topics increased over time. HOW: Both quantitative and qualitative study designs were reported, with quantitative observational designs being the most common (n = 174). WHY: Objectives indicated that studies were conducted for the benefit of humans (n = 187), animals (n = 130), physical environment (n = 55), social environments (n = 33), and plants (n = 4). Conclusion: This scoping review of primary OH research shows a diverse body of work, with human health being considered most frequently. We encourage continued knowledge synthesis work to monitor these patterns as global issues and the application of OH approaches evolve. Keywords: global One Health research, knowledge synthesis, one health applications, one health definitions.
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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 | 0.177 | 0.437 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.047 | 0.051 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.020 | 0.021 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.007 | 0.005 |
| 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".