A call for a shared future vision for Planetary and One Health Literacy
Bibliographic record
Abstract
Global health is increasingly shaped by interlinked crises such as climate change, biodiversity loss, pollution, and social inequalities, all of which undermine the determinants of health. At the same time, the digital revolution and geopolitical instability amplify misinformation and inequities. Health literacy has been recognized by the WHO Global Health Strategy as a key pillar of resilient health systems, while the Lancet One Health Commission highlights the urgent need for shared competencies across human, animal, and environmental health. Against this backdrop, the concepts of Planetary Health Literacy and One Health Literacy provide complementary frameworks to extend health literacy into ecological systems and the interconnected health of humans, animals, and other species. Planetary Health Literacy emphasizes sustainability and ecological boundaries, whereas One Health Literacy focuses on interspecies risks such as zoonoses and antimicrobial resistance. Together, they provide a powerful approach for fostering competencies that enable individuals, communities, professionals, and policy-makers to critically appraise information, make sustainable and health-promoting decisions, and act across human and ecological systems. This article calls for a shared vision of Planetary and One Health Literacy to guide health promotion, education, and policy. Key action priorities include embedding these literacies across all levels of education and professional training; developing and validating indicators for measurement; incorporating them into public health policies and climate-health frameworks; fostering cross-sectoral collaboration; and including indigenous and traditional knowledge. By investing in Planetary and One Health Literacy, governments and institutions can empower societies to adopt healthier, more sustainable behaviours, build climate-resilient health systems, and advance a systemic response to today's polycrisis.
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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 | 0.051 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.011 | 0.050 |
| Scholarly communication | 0.026 | 0.045 |
| Open science | 0.004 | 0.036 |
| Research integrity | 0.029 | 0.047 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".