A Transdisciplinary Perspective on Dog-Handler-Client Interactions in Animal Assisted Activities for Children, Youth and Young Adults
Bibliographic record
Abstract
Abstract A growing body of research has linked the inclusion of dogs in Animal-Assisted Activities (AAA) for children and young adults to a diverse range of positive social emotional and cognitive outcomes. However, many studies have focused exclusively on aspects directly related to dog-client interactions. There is a need to gain a better understanding of how dog-handler teams have been described, conceptualized and incorporated into the analysis in previous research. In addition, few studies have investigated the mutual adjustments inherent to dog-handler-client triadic relationships. This paper explores if and how the unique characteristics of dog-handler teams have been conceptualized and measured in previous studies. First, this paper undertakes a scoping review to map what, if any, characteristics of dogs, handlers, and dog-handler teams have been described and incorporated into the assessment of AAAs from 2004 to 2019 including: demographic characteristics, formal training and certification, handlers’ or dogs’ behavioral and physiological responses to AAAs, handlers’ roles during activities, and configuration of AAA teams. This scoping review also highlights key features of AAA teams requiring further investigation. In addition, this paper proposes the incorporation of a transdisciplinary framework to the analysis of AAAs. Such a holistic framework can inform the field of human-animal interactions by prioritizing a relational and contextual focus to the study of AAAs.
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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 | 0.010 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".