Animals in Healthcare Facilities: Recommendations to Minimize Potential Risks
Bibliographic record
Abstract
Animals may be present in healthcare facilities for multiple reasons.Although specific laws regarding the use of service animals in public facilities were established in the United States in 1990, the widespread presence of animals in hospitals, including service animals to assist in patient therapy and research, has resulted in the increased presence of animals in acute care hospitals and ambulatory medical settings.The role of animals in the transmission of zoonotic pathogens and cross-transmission of human pathogens in these settings remains poorly studied.Until more definitive information is available, priority should be placed on patient and healthcare provider safety, and the use of standard infection prevention and control measures to prevent animal-to-human transmission in healthcare settings.This paper aims to provide general guidance to the medical community regarding the management of animals in healthcare (AHC).The manuscript has four major goals:1. Review and interpret the medical literature regarding risks and evidence for animal-to-human transmission of pathogens in the healthcare setting, along with the potential benefits of animal-assisted activities in healthcare.2. Review hospital policies related to AHC, as submitted by members of the SHEA Guidelines Committee.3. Summarize a survey that assessed institutional AHC policies.4. Offer specific guidance to minimize risks associated with the presence of AHC settings.Recommendations for the safe oversight and management of AHC should comply with legal requirements and minimize the risk of transmission of pathogens from animals to humans when animals are permitted in the healthcare setting.Although little published literature exists on this topic, we provide guidance on the management of AHC in four categories: animal-assisted activities, service animals, research animals, and personal pet visitation.Institutions considering these programs should have policies that include well-organized communication and education directed at healthcare personnel (HCP), patients, and visitors.Appropriately designed studies are needed to better define the risks and benefits of allowing animals in the healthcare setting for specific purposes.
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".