Animal-assisted crisis response: Characteristics of canine handlers and their canine partners
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract As we encounter many disasters and crises worldwide, various forms of crisis intervention are utilized to assist those who are impacted. In the United States, animal-assisted crisis response (AACR), the use of highly trained and experienced therapy dogs to provide comfort and support to those in need, is becoming an essential post-crisis modality. However, maintaining qualified volunteers is challenging and research on the characteristics of AACR teams has been narrowly developed. Therefore, there is a growing need to better understand these specialized volunteer teams, both handlers and their canine partners. This exploratory survey of 99 animal-assisted crisis responders investigated the qualities, backgrounds, perceptions, and experiences of these canine handlers and their dogs. Results from an online questionnaire showed that most handlers were women (88%), and the prevalent age range was 61–70 (45%). Most handlers were retired (46.46%) with an average volunteer experience in AACR of 5.7 years. The recognition of dominant organizations providing AACR was also studied in this research. Most volunteers were members of HOPE AACR (54.36%). Handlers shared their education, specific traits, and skills to provide effective AACR. The characteristics of the canine partners of AACR were also explored. The most common breeds for AACR teams were Golden retrievers (28.88%) and Labrador retrievers (18.88%). The authors additionally explored the handlers and the traits of their dogs, as well as their reason for volunteering in AACR. All participants viewed AACR as effective with most of the teams (80.68%) perceiving AACR as a highly effective intervention following crises and disasters. This research offers insights for AACR organizations on strategies to recruit and retain these specialized crisis providers.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it