Bibliographic record
Abstract
Summary Historically, the oldest healers were often priests who cared for humans and animals, with which integrative thinking about human and animal health began long ago. Nowadays, human and animal medicine have developed their own autonomous domains. For which animal health deals specifically with communicable diseases (including zoonoses), food safety, management of animal populations (farm animals, companion animals, sports), animal welfare and environmental problems related to animals. As a result of this separation, human and animal diseases are treated as separate entities, which has led to reduced communication between the human health domain and the animal health domain. In recent times the One Health (OH) approach illustrates how the connection and interrelationship between health of people, animals and plants has impact on Global Health (GH). Transmission of microorganisms takes place in and between the three domains of people, animals, and plants, within which all organisms in ecosystems live together. Cooperation between all domains and economic sectors is therefore important pillar of the OH approach. The most accessible application of the OH approach relates to immunity and has led to main interventions of large-scale immunization efforts through vaccinations. Although vaccines are a small part of the OH approach, they are important in crises where they play a key role in controlling the transmission of diseases between humans and animals and have a concrete impact on their shared environment and contribution to global health. However, collaboration among the wide diversity of stakeholders and steps towards implementing the OH approach faces many challenges. Professionals in both the human and animal domains have developed knowledge and gained experiences that can be used as input for learning and working together to create solutions for GH challenges. Therefore, the question rises in what situations, based on the OH approach, does collaboration between the two domains is adding value to the creation of GH solutions? Should there be collaboration in all situations and at all times e.g. for less obvious applications, like vaccine development for non-zoonotic pathogens? The aim of this thesis is therefore to create an overview of barriers and facilitators for knowledge sharing and collaboration between the human and animal domain by answering the following question: What are the opportunities, barriers, and approaches for collaboration, based on the OH approach, and how are they acted upon in practice especially when developing vaccines for nonzoonotic pathogens?
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 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".