Transdisciplinary and social-ecological health frameworks—Novel approaches to emerging parasitic and vector-borne diseases
Bibliographic record
Abstract
Ecosystem Health, Conservation Medicine, EcoHealth, One Health, Planetary Health and GeoHealth are inter-related disciplines that underpin a shared understanding of the functional prerequisites of health, sustainable vitality and wellbeing. All of these are based on recognition that health interconnects species across the planet, and they offer ways to more effectively tackle complex real-world challenges. Herein we present a bibliometric analysis to document usage of a subset of such terms by journals over time. We also provide examples of parasitic and vector-borne diseases, including malaria, toxoplasmosis, baylisascariasis, and Lyme disease. These and many other diseases have persisted, emerged or re-emerged, and caused great harm to human and animal populations in developed and low income, biodiverse nations around the world, largely because of societal drivers that undermined natural processes of disease prevention and control, which had developed through co-evolution over millennia. Shortcomings in addressing drivers has arisen from a lack or coordinated efforts among researchers, health stewards, societies at large, and governments. Fortunately, specialists collaborating under transdisciplinary and socio-ecological health umbrellas are increasingly integrating established and new techniques for disease modeling, prediction, diagnosis, treatment, control, and prevention. Such approaches often emphasize conservation of biodiversity for health protection, and they provide novel opportunities to increase the efficiency and probability of success.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.034 | 0.034 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".