Understanding Psychiatric Patients’ Experience of Virtual Animal-Assisted Therapy Sessions during the COVID-19 Pandemic
Bibliographic record
Abstract
Canine animal-assisted therapy (AAT) can improve the mental health and well-being of incarcerated individuals. An in-person AAT program has been offered at the Regional Psychiatric Center (RPC) in Saskatoon, Canada, since 2014 with St. John Ambulance Therapy Dog Program (SJATDP) dog and handler teams. The program transitioned, for the first time, to a virtual format with the onset of the COVID-19 pandemic in March 2020. This exploratory research examines whether and how a virtual offering of AAT at RPC can provide positive benefits to forensic psychiatric patients. Overall, the findings reveal an understanding of the virtual sessions from patient, handler, and clinician perspectives, including (a) differences between connection in virtual versus in-person facilitation, (b) the role of technology, (c) the unique role of the handler, and benefits for patients, including (d) emotional support, (e) positive effects on mental health, and (f) feelings of hope, normalcy, and deinstitutionalization despite the COVID-19 pandemic. Using an online platform allowed patients who had had preexisting interactions with the therapy dog teams to form or continue their connection/bond and benefit from AAT during the COVID-19 pandemic, a time when in-person contact was not possible. Therefore, this research provides support for the use of web-based video conferencing in facilitating AAT sessions with incarcerated psychiatric patients.
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".