10 The Impact of Global Disasters on Our Pets: Lessons from COVID-19
Bibliographic record
Abstract
Abstract The coronavirus (COVID-19) pandemic provided a unique insight into the impacts of global disasters on pet ownership and companion animal services. This talk will review research on the impacts of various stages of the pandemic on the human-animal relationship, surprising increases in the adoption of pets from animal shelters around the world, and the potential reasons for those increases. I will also present new research on the impact of the pandemic on pet support services within the city of Vancouver, Canada, that will highlight the complex relationships between vulnerable human populations, the city’s response to the pandemic, and pet care. Finally, the COVID-19 pandemic, and its associated economic impact, have completely re-shaped the field of animal sheltering and companion animal support services. In addition to being recognized as an essential service, animal shelter and veterinary staff were confronted with the need to identify only necessary operations to ensure care of animals and their communities without the risk of contracting and transmitting the virus. As a result, emergent animal sheltering trends now emphasize community-based approaches, abolishing harmful discriminative practices, and aiming to keep pets and their original owners together – all from the framework of One Health/ One Welfare. As global disasters are projected to increase in frequency due to climate change, a better understanding of impacts on the human-animal bond and support services will ensure that we can be better prepared for the future.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".