“He's opening everybody's heart up”: Co-creating pet therapy activities with inpatients at a large urban mental health and addictions hospital
Bibliographic record
Abstract
Background Animal-Assisted Activities (AAA) is now ubiquitous in many health care settings around the world. There is growing evidence about AAA's benefits but there is no literature about the inclusion of patients, the end-user, in the design and improvement of these programs. The aim of this study was to co-create AAA activities with inpatients to make their hospital experience in the psychiatric milieu more humanizing. Methods Informed by the principles of Participatory Action Research, we conducted seven focus group discussions with 38 participants in seven different inpatient units at a large urban mental health and additions hospital. In these discussions we asked participants what made pet therapy meaningful for them, the activities they enjoyed, what kind of activities they would like to see more of, and if they had experienced any barriers to their participation in this voluntary, recreational program. Results Our results show that AAA has four entangled elements: the bidirectional relationship between participant and therapy dog; the capacity of therapy dogs to generate social interactions, a tension between structured and unstructured activities with animals; and therapy dogs as a vector for a deeper relationship with nature. Conclusion Intentional patient-derived changes to AAA programs would amplify their impact for inpatients in psychiatric care. Providing both human (e.g., clinical support) and non-human (e.g., dog treats) would support patients in their desire to develop deeper relationships with their animal companions. Further, while most research to date emphasizes the therapeutic benefits of AAA, our results demonstrate that animal-led activities that result in positive social interactions with others (including non-human animals) were the most meaningful to participants.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".