Fostering Connections in CAIs: Handlers’ insights around how to optimize university student-therapy dog interactions
Bibliographic record
Abstract
Research has established that interacting with therapy dogs can elicit improved well-being of clients in a variety of contexts. Despite the growing literature attesting to the benefits of human-canine interactions in canine-assisted interventions (CAIs), little empirical attention has been dedicated to understanding the mechanisms within these interactions. The aim of this study was to gain, through open-ended, qualitative prompts, handlers’ insights around how to optimize client-therapy dog interactions. Handlers involved in two Canadian programs (“PAWS your stress” at the University of Saskatchewan and B.A.R.K. at the University of British Columbia; N = 62) completed qualitative surveys asking them questions about the bonds observed and connections facilitated between their dogs and the university students they visited with. Responding to the question “Is there something you do to facilitate a connection between your dog and the client they are visiting with?”; participants’ responses revealed that handlers utilized a variety of skills or techniques. Prominent themes that emerged were that handlers would: 1) Encourage students to pet their dog and guide these interactions based on the dog’s preferences; 2) Allow their therapy dog partners to work intuitively; 3) Encourage and give permission for the dog to approach the client; 4) Introduce their dog and share information about their personality; and 5) Adjust the interaction between dog and client based on the client’s familiarity and comfort with dogs. These findings hold implications for volunteer handlers working in therapy dog organizations, as well as program directors or coordinators in charge of post-secondary and other CAIs.
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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 | 0.018 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".