Responsibility-sharing for pets in disasters: lessons for One Health promotion arising from disaster management challenges
Bibliographic record
Abstract
During disasters, the behaviour of pet owners and of pets themselves may compromise the ability of emergency responders to perform their duties safely. Furthermore, pet loss can have deleterious effects on personal and community recovery. To explore these issues and their implications for health promotion and disaster management practice, we conducted semi-structured interviews with 27 emergency responders in Australia, where disaster policy embraces shared responsibility yet does not acknowledge pets. We found that responders commit to being responsible for protecting human lives, especially members of their teams. Frontline emergency responders did not regard pets as their responsibility, yet decisions made with tragic consequences for pets exacted an emotional toll. Emergency managers consider community education as a pivotal strategy to support building people's capacity to reduce their own risk in disasters. While important, we question whether this is sufficient given that human life is lived in more-than-human contexts. Reformulating the parameters of the Ottawa Charter for Health Promotion as 'One Health Promotion' may help to account for the intermeshed lives of people and pets, while acknowledging human priority in public policy and programming. To acknowledge the influence of people's pets in disaster responses and recovery, we recommend five overlapping spheres of action: (i) integrate pets into disaster management practice and policy; (ii) create pet-friendly environments and related policies; (iii) engage community action in disaster management planning; (iv) develop personal skills by engaging owners in capacity building and (v) reorient health and emergency services toward a more-than-human approach.
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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 | 0.031 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.026 |
| Scholarly communication | 0.010 | 0.019 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.008 | 0.016 |
| 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".