Companion animal veterinarians discuss aspects of one health with pet owners during most veterinary appointments
Bibliographic record
Abstract
OBJECTIVE: To examine the prevalence and context of one-health conversations between veterinarians and clients in companion animal practice. SAMPLE: A random selection of 60 companion animal veterinarians; a convenience sample of 917 interactions from Southern Ontario, Canada. Of these, 100 audio-video-recorded interactions including 47 of 60 veterinarians were randomly selected for inclusion in this study. METHODS: Audio-video recordings were made of veterinarian-client-patient interactions between November 2017 and January 2019. A researcher-generated coding framework was developed and used to assess the prevalence and content of one-health topics communicated during veterinary appointments. RESULTS: Of the interactions assessed, 60 were preventive care and 40 were health problem appointments. Further, 78% (78/100) included at least 1 discussion related to one health. One-health topics included zoonoses (28% [28/100]), animal behavior (25% [25/100]), illness/disease (20% [20/100]), activity level/exercise (16% [16/100]), nutrition (16% [16/100]), dentistry (6% [6/100]), body weight (3% [3/100]), animal welfare (3% [3/100]), dog/cat bites (2% [2/100]), cannabis (2% [2/100]), and aging (1% [1/100]). Zoonotic diseases were mentioned in 65 appointments, 28 of which evolved into a one-health discussion. Antibiotics were discussed in 27 appointments, none of which were discussed in relation to one health (eg, antimicrobial resistance). CLINICAL RELEVANCE: Findings suggest that one-health topics are raised within most veterinary appointments. Opportunities exist for more comprehensive one-health conversations between veterinarians and their clients, particularly in relation to zoonotic diseases and antimicrobials.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".