Exploring the Social Determinants of Animal Health
Bibliographic record
Abstract
An understanding of the One Health and EcoHealth concepts by students is dependent on medical pedagogy and veterinary medical pedagogy having similarities that allow a common discourse. Medical pedagogy includes a focus on the social, political, and economic forces that affect human health, while this discourse is largely absent from veterinary medical pedagogy. There is, however, a gradient in health that human and animal populations experience. This health gradient in human populations, which runs from low to high according to the World Health Organization, is largely explained by “the conditions in which people are born, grow, live, work, and age.” 1 , 2 Regarding the human health gradient, other authors have broadened the list of conditions to include access to health care systems used to prevent disease and treat illness, and the distribution of power, money, and resources, which are shaped by social, economic, and political forces. 1 , 2 In human medicine, these conditions are collectively termed the social determinants of health (SDH). Veterinarians who work with the public encounter people and their animals at both the low and the high end of the health gradient. This article explores the concept of the parallel social determinants of animal health (SDAH) using examples within urban, rural, and remote communities in North America as well as abroad. We believe that in order to understand the One Health paradigm it is imperative that veterinary pedagogy include information on, and competence in, SDH and SDAH to ultimately achieve improvements in human, animal, and environmental health and wellbeing.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".