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Record W7025341706

Understanding Psychiatric Patients’ Experience of Virtual Animal-Assisted Therapy Sessions during the COVID-19 Pandemic

2022· article· en· W7025341706 on OpenAlexaboutno aff

Bibliographic record

VenuePurdue e-Pubs (Purdue University System) · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPandemicMental healthFeelingVideoconferencingExploratory researchCoronavirus disease 2019 (COVID-19)TelemedicineMental illness
DOInot available

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.461
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.072
GPT teacher head0.307
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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