Human–Animal Interactions in Disaster Settings: A Systematic Review
Bibliographic record
Abstract
Abstract This systematic review aimed to assess the current knowledge of human–animal interactions (HAIs) in disaster settings and identify areas for future research. A Preferred Reporting Items for Systematic Reviews and Meta-Analyses search was conducted on three multidisciplinary databases, identifying English-language journal articles published between January 2000 and February 2022 that explored the benefits of and challenges associated with HAI in disasters and emergencies. The review analyzed 94 articles using both quantitative and qualitative methods. The review found a paucity of universal terminology to describe the bidirectional relationship between humans and animals during disasters and a failure to include all animal types in every stage of disaster and emergency management. Additionally, research predominantly focused on the health and well-being benefits of HAI for humans rather than animals. Efforts to promote social and environmental justice for humans and their co-inhabitants should support the welfare of both humans and animals in disaster settings. Four recommendations were developed based on these findings to increase the inclusion of HAI in research, policy, and practice. Limitations of the review included the exclusion of pre-2000 articles and all grey literature, limited research examining different combinations of animal and disaster types, and limited research outside of North America.
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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 | 0.012 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".