Barriers and facilitators to implementing veterinary telemedicine in animal production
Bibliographic record
Abstract
In France, in recent years, the field of veterinary medicine has witnessed a growing interest in veterinary telemedicine, driven by rapid technological advancements and a decline in the availability of veterinarians, particularly in remote and rural areas. However, there is a scarcity of literature addressing the barriers and facilitators of implementing telemedicine in veterinary practice. Therefore, this study aims to investigate the factors that influence the adoption of veterinary telemedicine for bovine, poultry and swine in France. Insights from both farmers and veterinarians were collected and subjected to qualitative analysis utilizing the COM-B model of behavior change. Significant barriers and facilitators were identified. Major hurdles encompass technological limitations, regulatory complexities, and concerns regarding the evolution of the veterinary profession. Conversely, the expertise of veterinarians, coupled with their understanding of their clients' farms, and the potential for remote interventions, emerged as primary facilitators. The study emphasizes the critical role of regulation in ensuring ethical standards and maximizing the benefits of telemedicine. With clear regulatory frameworks in place, telemedicine holds promise for enhancing animal health and optimizing veterinary practice.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".