Bibliographic record
Abstract
The popularity of having exotic animals as pets is increasing, particularly among children. It is also estimated that approximately 75% of emerging infectious diseases are zoonotic. The implications of these two trends are areas of concern for the public health community. We conducted a review of household pet zoonoses studies. This included a jurisdictional scan of public health agencies in Canada for policies and protocols on household pet zoonoses. Key stakeholder consultations with pet-related zoonoses experts and authors in Canada enhanced the information reviewed. Trends in pet ownership, risks of disease transmission, burden of illness, and current public health practices were examined. As a result, policy and intervention gaps and future opportunities for research and collaboration were identified. Specifically, pets remain as a primary source of numerous reportable and nonreportable diseases and outbreaks for example, salmonellosis, tularaemia, cutaneous larvae migrans, and Human Lymphocytic Chorimeningitis Virus infections. Pet treats and some pet foods were cited as potential sources of zoonotic diseases. Children under 5 years of age and immuno-compromised individuals were noted as potential high-risk groups; and daycares, schools, summer camps, private homes, and acute care and veterinary hospitals were noted as high-risk settings for zoonotic disease transmission. The primary risk factors identified include improper handling of pets and improper hand hygiene. The continued growth of the pet industry will necessitate interventions by public health, veterinary, and regulatory communities to mitigate the impact of pet zoonoses on the public. These interventions should include enhancement of the current surveillance systems, regulations to address existing gaps in the pet food industry, the development of policies and protocols at the provincial and federal levels of government, education of the public regarding the risks associated with the handling of pets, and greater collaboration among the human and animal health sectors.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".