Exploring the nexus: Comparing and aligning Planetary Health, One Health, and EcoHealth
Bibliographic record
Abstract
The interconnectedness between humans and ecosystems highlights the need to protect ecosystems for the well-being of humans and the environment. This has led to the emergence of holistic and interdisciplinary concepts like Planetary Health, One Health, and EcoHealth. There is a growing interest in the differences and implementation of these concepts, including their founders, fundamental questions answered, focus, global distribution of studies, and alignment. This study addresses these issues to facilitate coordinated health interventions for people and ecosystems. Using electronic databases (Web of Science, PubMed, and ProQuest) and conducting a systematic literature review using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), this paper compares the concepts of Planetary Health, One Health, and EcoHealth, providing a comprehensive overview of the findings and insights by examining each field's advocacy, conceptual application, and implementation levels and exploring the contributions of influential individuals and organizations. The results highlight each concept's global relation to applicability, challenges, and opportunities for further advancement. The study concludes by emphasizing the shared goals and interconnections among these fields in addressing complex health issues at the nexus of human health, environmental health, and ecosystem well-being.
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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 | 0.124 | 0.230 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.016 | 0.019 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".