Introducing ‘One Health’ as an overlooked concept in Iran
Bibliographic record
Abstract
Introducing 'One Health' as an overlooked concept in IranDear Editor, 'One Health' is the "collaborative effort of multiple disciplines -working locally, nationally, and globally-to attain optimal health for people, animals and our environment" (1).The concept of 'One Health' is not as new as it may seem at the first glance, as its pioneer supporters used to live in the 19 th century.Looking back in history, Louis Pasteur and Robert Koch's achievements are good examples of practicing 'One Health' (2).More recently in 1940s, efforts of Dr. Steele and his peers around the globe in developing the first 'Veterinary Public Health' program made rapid advances in the control and prevention of zoonotic diseases, both in the United States and globally (2).The interaction of humankind, environment, and animals has led to a dynamic through which the health of these groups is interrelated.The scope of 'One Health' is remarkable, wide, and rapidly growing.It is a pity that the way we treat the environment has lessened its health.This contamination and pollution would lead to creating a favorable setting for micro-organisms and expansion of infectious diseases damaging the health of both animals and humans (3).Of around 1400 infectious diseases recognized in humans, almost 60% are due to multi-host pathogens moving across species lines (4).Furthermore, around 75% of new emerging and re-emerging human infectious diseases in the last 30 years have been zoonotic ( 5).Several examples could address the importance of veterinary medicine in increasing the health of humans; from the spread of AIDS-related infections (i.e.tuberculosis) to the outbreaks of Severe Acute Respiratory Syndrome (SARS), avian flu, monkey pox, and Crimean-Congo Hemorrhagic Fever (CCHF) around the globe (6).All in all, the complete health of a community would only be achieved by calling for an integrated approach addressing the health of not only human and animals, but also and the ecosystem they reside in.The health of these three groups are highly inter-connected and should not be separated (7).Despite the growing interdependence of humans with animals and their products and in turn the probable threats against our health, veterinary and human medicines are still viewed
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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 | 0.013 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.018 | 0.032 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".