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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".