The Impact of Therapy Dog Visitation For Emergency Department Patients with Mental Health Concerns
Bibliographic record
Abstract
In Canada, emergency departments have a large amount of mental health and substance use patients passing through their doors daily. As such, our research sought to assess the impact that therapy dog visits had for patients presenting with mental health and/or substance use concerns to a Canadian emergency department, as well as their significance for the staff working within the space. Working alongside an active patient advisory group and community-based therapy dog program volunteers, our mixed method approach utilized ethnography (60 hours institutional observation), surveys, and interviews over a three-month period. Grounded in a One Health framework and inspired by a critical qualitative health methodology, we conducted 28 in-depth interviews with patients and employed an affective coding method to this data, which was then triangulated with, and supported by, the ethnographic and survey data. Findings showed that therapy dog visitation had a positive impact on both patients and staff, thus improving the overall patient experience. Our study displays how the patient experience was impacted via improved communication; a decrease in agitation or distress; providing a sense of connection from non-judgemental support; an increase in hope and optimism; and a welcomed, calming distraction from local stressors. Further, staff also received benefit in their working conditions where they considered the therapy dog a member of the care team. This work outlines how including therapy dog visits as a complementary aide in existing provisional care provides a more holistic and empathetic approach to mental health and substance use responses in the Canadian context, which is particularly relevant in busy, high-stress emergency department settings. Further research must address the separate impact that the therapy dog handler has over the therapy dog itself.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".