The environment in global health governance: an analysis of environment-related resolutions adopted at the World Health Assembly from 1948 to 2023
Bibliographic record
Abstract
BACKGROUND: The concept of planetary health underscores the intricate relationship between environmental concerns and global health. This interconnection raises an important question related to cross-sectoral policy development: to what extent are environmental issues integrated into global health governance? To address this question, this study examines resolutions adopted by the World Health Assembly (WHA) from 1948 to 2023. METHODS: Based on a systematic text search for environmental issues, this study examines the evolution of the occurrence and content of resolutions adopted by the WHA and the structure and pattern of connectivity of the normative network of resolutions regarding environment-related resolutions from 1948 to 2023. Environment-related resolutions were processed in the Python environment using relevant packages, such as Pandas, Numpy, and Matplotlib. Regular expressions were employed to identify citations among resolutions and construct a directed citation network. The network was then examined using NetworkX and Graph-Tool. FINDINGS: Despite important variations in the attention dedicated to environmental issues in resolutions adopted by the WHA, the proportion of environment-related resolutions adopted each year has increased. The number of topics and their diversity have also expanded. Although environment-specific resolutions are well connected to each other, they are more weakly connected to environment-related resolutions, and not well connected to non-environment-related resolutions, suggesting potential silos in policy development. This study shows that several topical entry points exist for a deeper integration of environmental concerns in global health governance. INTERPRETATION: The findings of this study indicate not only the growing reference to environmental concerns in global health governance, but also an evolution of the understanding of the environment as a key driver of the health of the people. However, there remains room for more comprehensive integration across all areas of global health policy. The study emphasises both the need for active participation in global environmental governance processes that affect health and the importance of minimising the health sector's contribution to environmental problems. FUNDING: None.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.023 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".