Veterinarians are reluctant to recommend virtual consultations to a fellow veterinarian
Bibliographic record
Abstract
OBJECTIVE: To explore veterinarians' use of virtual veterinarian-client-patient consultations before and during the coronavirus disease 2019 (COVID-19) pandemic and examine veterinarians' attitudes toward virtual consultations. SAMPLE: 135 companion animal veterinarians in Canada, the US, and Europe. METHODS: An anonymous online survey was distributed to gather participating veterinarians' use of information and communication technologies and their perception of virtual consultations' effect on patient care, client communication, and their own well-being. Willingness to recommend virtual consultations was evaluated using the Net Promoter Score. Multivariable logistic regression explored factors associated with willingness to recommend virtual consultations. RESULTS: Percentage of participating veterinarians using the telephone and videoconferencing increased significantly (P < .001) from before (83.6% and 3.0%, respectively) to during the COVID-19 pandemic (97.0% and 22.4%, respectively). Participants were significantly less confident (P < .001) about their ability to reach a diagnosis using a virtual consultation as compared to a hands-on patient examination. Participants perceived client communication to be more challenging during virtual as compared to face-to-face consultations, particularly for building rapport and expressing empathy. Participants were extremely unwilling to recommend virtual consultations (Net Promoter Score = -41.4) with 21.6% (24/111) promoters and 63.1% (70/111) detractors. Confidence doing a virtual patient examination and comfort using videoconferencing technology were both positively associated (P < .05) with willingness to recommend virtual consultations. CLINICAL RELEVANCE: Veterinary practices and organizations interested in encouraging virtual veterinarian-client-patient consultations likely need to prioritize veterinarians' acceptance as an initial focus. The veterinary profession would benefit from further research and education to inform virtual veterinary care.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.007 | 0.001 |
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".