A Survey of Canadian Public Health Personnel Regarding Knowledge, Practice and Education of Zoonotic Diseases
Bibliographic record
Abstract
Zoonoses, diseases that can spread under natural conditions between humans and other animals, are become a major public health concern in many countries including Canada. In Canada, investigations of zoonotic disease incidents are often conducted by public health inspectors (PHIs). However, little is known about PHIs' knowledge of transmission of zoonotic pathogens, their perceptions of zoonotic disease importance or their education regarding zoonotic diseases. The objective of this study was therefore to assess the knowledge, perceptions and education of Canadian PHIs regarding zoonotic diseases. Data were collected from December 2008-January 2009 using an internet-based survey distributed to members of the Canadian Institute of Public Health Inspectors national listserv. Responses were received from 229 PHIs in four provinces, with a response rate of approximately 20%. The majority of respondents reported at least 10 years of experience in the public health sector, 80% (181/225) were in frontline positions, and 62% (137/222) were routinely involved in investigations of infectious diseases. Two-thirds believed that the importance of zoonotic diseases with regards to public health would increase in the next 5 years. Whilst most respondents were able to correctly identify animals capable of directly transmitting common zoonotic pathogens, there were gaps in knowledge, particularly with regard to rabies and transmission of gastrointestinal pathogens by companion animals. PHIs tended to feel that their training on zoonotic diseases prior to working as PHIs was deficient in some areas, or left some room for improvement. Their responses also suggested that there is a need for improvement in both the quantity and the quality of continuing education on zoonotic diseases. In particular, less than one-third of PHIs received ongoing continuing education regarding zoonotic diseases, and of those that did, nearly two-thirds rated the quantity and quality as only fair.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".