Beyond the campus context: Reducing stress among students and community members through virtual canine comfort modules
Bibliographic record
Abstract
Abstract Research continues to demonstrate the stress-reducing benefits of in-person interactions with therapy dogs, especially among students engaged within educational contexts. However, less is known about the potential of virtual interactions with therapy dogs to reduce stress among post-secondary students and if such benefits might extend to members of the general community. Accordingly, the aim of this study was twofold. First, we explored how a virtual canine-assisted stress-reduction intervention might support well-being among post-secondary students and, second, we extended the model to explore if the intervention might support well-being among non-student community members. Using a two-phased method, we assessed the effects of a 5-min asynchronous virtual canine-assisted intervention (CAI) on self-reports of stress in both students (Phase I; N = 963) and community members (Phase II; N = 122). Results revealed that spending as little as 5 min viewing virtual canine comfort modules significantly reduced participants’ pre-to-post-test self-perceived stress among both post-secondary students and non-student community members. Further, among post-secondary students, women experienced greater reductions in stress compared to male participants. Before the CAI, women had higher stress levels compared to men, but after the CAI, women and men had similar stress levels. The results did not reveal age to be significantly related to the magnitude of student participants’ self-reported stress reduction. Our results have implications both for the field of human-animal interactions and for the delivery of mental health interventions that are low-cost, low-barrier, and easily accessible to diverse individuals.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".