Characteristics of Programs Involving Canine Visitation of Hospitalized People in Ontario
Bibliographic record
Abstract
OBJECTIVES: To determine the distribution of canine-visitation programs in Ontario and to characterize the nature of the programs the dogs are affiliated with. DESIGN: A cross-sectional survey of hospitals in Ontario was used to determine whether they permitted dogs to visit patients and, if so, where the dogs originated. On the basis of this information, dog owners were then contacted through their respective associations and interviewed using a standardized questionnaire. SETTING: A cross-section of hospitals in Ontario. PARTICIPANTS: A total of 223 (97%) of the 231 hospitals surveyed responded. Ninety owners of 102 visitation dogs were interviewed. RESULTS: A total of 201 (90%) of the 223 hospitals indicated that dogs were permitted in their facilities. Origins ranged from national therapy-dog agencies to the patients' families. Acute care wards were 5.1 times as likely than other wards to disallow animals (95% confidence interval, 2.2-12.2; P<.001). According to the 90 dog owners included in the study, the screening protocols that dogs were required to pass to participate in their respective visitation programs were highly variable, as were the owners' infection control practices. Eighteen owners (20%) said they did not practice any infection control. Sixty-six owners (73%) allowed their dogs on patients' beds, and 71 (79%) let their dogs lick patients. Thirty-six owners (40%) were unable to name one zoonotic disease that may be transmitted from their dog. CONCLUSIONS: Although canine-visitation programs have become standard practice in nonacute human healthcare facilities, infection control and dog-screening practices are highly variable and potentially deficient. Hospital staff, visitation groups, pet owners, and veterinarians need to work together to protect both people and pets.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".