Patients Without Borders: Using Telehealth to Provide an International Experience in Veterinary Global Health for Veterinary Students
Bibliographic record
Abstract
There is an increasing need to produce veterinarians with knowledge and critical thinking skills that will allow them to participate in veterinary global health equity delivery, particularly in the developing world, where many people remain dependent on animal-based agriculture for a living. This need for veterinarians trained in global health is reflected by the demand among students for greater exposure and education. At the same time, many students are held back from on-site training in global health due to constraints of cost, time, or family obligations. The purpose of this article is to describe the use of a telemedicine approach to educating veterinary students at Tufts Cummings School of Veterinary Medicine. This approach simultaneously provides expert consultation and support for a pro bono hospital in the developing world. The development of a telemedicine teaching service is discussed, from initial ad hoc email consultation among friends and associates to a more formal use of store-and-forward delivery of data along with real-time videoconferencing on a regular basis, termed tele-rounds. The practicalities of data delivery and exchange and best use of available bandwidth are also discussed, as this very mundane information is critical to efficient and useful tele-rounds. Students are able to participate in discussion of cases that they would never see in their usual clinical sphere and to become familiar with diagnostic and treatment approaches to these cases. By having the patient "virtually" brought to us, tele-rounds also decrease the usual carbon footprint of global health delivery.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".