A Commentary about Lessons Learned: Transitioning a Therapy Dog Program Online during the COVID-19 Pandemic
Bibliographic record
Abstract
In 2015, the University of Saskatchewan PAWS Your Stress Therapy Dog program partnered with St. John Ambulance for therapy dog teams to visit our campus and offer attendees love, comfort and support. We recognized at the start of the COVID-19 pandemic that students, staff and faculty may require mental health support, particularly with the challenges of isolation and loneliness. In response, our team transitioned from an in-person to a novel online format at the start of the COVID-19 pandemic. We designed online content for participants to (1) connect with therapy dogs and experience feelings of love, comfort and support as occurred in in-person programming, and (2) learn about pandemic-specific, evidence-informed mental health knowledge. Our unique approach highlighted what dogs can teach humans about health through their own care and daily activities. From April to June 2020, we developed a website, created 28 Facebook livestreams and 60 pre-recorded videos which featured therapy dogs and handlers, and cross-promoted on various social media platforms. Over three months, first a combined process-outcome evaluation helped us determine whether our activities contributed to the program's goals. A subsequent needs assessment allowed us to elicit participant preferences for the program moving forward. This commentary reflects on these findings and our teams' collective experiences to share our key lessons learned related to program personnel needs, therapy dog handler training and support requirements, and online programming prerequisites. This combined understanding is informing our current activities with the virtual program and should be of interest to other therapy dog programs transitioning online.
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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 | 0.020 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.011 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.046 | 0.059 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".