Client knowledge, attitudes and practices regarding zoonoses: a metropolitan experience
Bibliographic record
Abstract
OBJECTIVE: To assess knowledge, attitudes and practices in relation to zoonoses among pet owners. METHODS: Questionnaire completed by 81 clients attending a small animal practice in Sydney, Australia. RESULTS: Most (64.5%) clients reported that they were not concerned about contracting a disease from their pet, but 7.9% and 3.9% of clients were a little or very concerned, respectively; 23.7% of clients stated that they had not considered the possibility. Although respondents indicated that they had heard of a number of zoonoses, knowledge of animal sources and exposure pathways was generally low, particularly for the more important zoonoses in Australia such as toxoplasmosis, psittacosis and Q fever. Only 37.0%, 12.3% and 11.1%, respectively, of clients had heard of these diseases. Most respondents (84.1%) indicated that they viewed veterinarians as having the primary responsibility for providing information about zoonoses, yet less than half (48.1%) recalled ever getting information from their veterinarian. Likewise, many respondents (48.1%) indicated that medical professionals played a role in providing information about zoonoses, yet less than one-quarter (23.5%) recalled ever getting information from their doctor. CONCLUSION: The low level of knowledge among pet owners about sources and exposure pathways indicates a need to strengthen communication between veterinarians, doctors and their clients around the possible risks of zoonoses and appropriate prevention strategies.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".