A case study of the patient wait experience in an emergency department with therapy dogs
Bibliographic record
Abstract
The quality of patient healthcare is a growing concern in Canada’s hospital emergency departments (ED) due to increasing wait times and associated adverse outcomes. A developing body of literature indicates that therapy dogs can positively impact the patient experience. In 2016, members of our team partnered with the Royal University Hospital (RUH) in Saskatchewan to become the first ED in Canada to integrate a visiting therapy dog to positively impact the patient wait experience. The aim of this preliminary case study was to examine if and how this unique initiative impacted patients’ feelings during their ED wait. A brief questionnaire was completed with one-hundred and twenty-four patients pre and post-therapy dog visit and a research observer documented the encounters. Quantitative and qualitative analysis of the data revealed that visiting with a therapy dog in the ED appeared to improve patients’ feelings. Specifically, patients’ perceived comfort levels increased and their distress levels decreased, and the encounters were considered by patients to be a welcome distraction from the stressful ED environment. Our team, comprised of clinicians, researchers, therapy dog handlers and patient advocates documented the advantages and challenges of implementing the initiative. The outcomes support further study of patients’ wait time experiences in the ED and the utility of a visiting therapy dog.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".