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 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 | 0.014 | 0.042 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".