Up the leash: Exploring canine handlers’ perceptions of volunteering in canine-assisted interventions
Bibliographic record
Abstract
ABSTRACT To date, research on canine-assisted interventions has focused on identifying the effects of spending time with therapy dogs on the well-being of participants and, to a lesser extent, exploring the effects of canine-assisted interventions on therapy dogs as a means of safeguarding canine welfare. Little empirical attention has focused on understanding the experience of volunteer canine handlers – agents at the heart of the success of canine-assisted interventions. The aim of this exploratory research was to first capture the voice of handlers to better understand their experience as volunteers and second to provide preliminary insights into their well-being. Sixty volunteer handlers with varying volunteer experience with a canine therapy program at a mid-size Canadian university responded to a series of open-ended prompts related to their volunteer work and completed a battery of well-being measures. Qualitative findings revealed that most participants identified social benefits to volunteering for themselves (64%) and for their dog (55%). The perceived impact on students (33%) and the ability to help university students (36%) were the most rewarding aspects of volunteering. Although drawn to volunteer by the program itself (36%), motivations to continue volunteering were predominantly associated with personal benefits of volunteering (44%). Most handlers reported no challenges associated with volunteering (73%) and qualified their dog as happy after sessions (71%). Participants commonly described good therapy dogs as relaxed, calm, and respectful (66%) and strong handlers as having good awareness of their dog (48%). Quantitative findings revealed volunteer handlers reported elevated levels of positive affect (p = < 0.001, d = 1.19), greater satisfaction with life (p = < 0.001, d = 0.85) and lower levels of avoidant attachment to their therapy dog (p = < 0.001, d = -1.16) when compared to normative samples. Implications for the governing of on-campus programs and handler well-being are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".