Gender Issues in Human, Animal and plant health using an Ecohealth Perspective
Bibliographic record
Abstract
The ecohealth approach is a core concept integrating environmental aspects with human and animal health (domestic and wild animals). Zoonotic and emerging diseases affect human health and impact negatively on food security. Although both the risk of contracting a disease and the subsequent impacts vary between different genders, age groups, cultures and social conditions, very little research has been done on this and few guidelines or interventions focus adequately on these aspects. This paper is based on a conceptual framework identifying the relationship between gender inequalities and the risk of contracting a disease in an ecohealth perspective. It looks at the varying impacts of plant, animal and human diseases and identifies four contributing factors. This paper first discusses the socially defined roles including social, economic, cultural, legal and political factors that often determine which place men and women occupy in society, which animals and plants men or women have accumulated knowledge of, which they have control of and which they benefit from and consequently the impact men and women have on the environment due to these specific roles. Secondly, it analyses the gender differences in risk of infection. It also analyses cultural differences that influence practices connected to animal, plant and human diseases and discusses respective preventions and treatments. Thirdly, it also identifies the ways men and women are impacted by the diseases of human, plants and animals in different ways. Lastly, this paper assesses the biological factors that influence the differences in exposure, infection rates and mortality rates between men and women during their life cycle. These four factors contribute to gender variations in relation to animal, human, plant and ecological health.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 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".