Advocating for incorporating public veterinary health care into human health systems
Bibliographic record
Abstract
In the first study to examine veterinary services in rural Alaska, this paper focuses on the use of a public health veterinary program embedded in the human health care system as a means of improving human health, specifically in rural villages such as the Yukon-Kuskokwim Delta of Alaska (YK Delta). The YK Delta is home to approximately 23,000 people living in 58 distinct rural communities, each of which is a federally recognized Tribe. Dogs play a particularly important role in these tribal communities. However, access to veterinary services is extremely limited in the region. Bringing together data from the Hub Outpost Project (HOP) which was designed to deliver preventative, public health veterinary services throughout the region, a meta-synthesis on relevant topics in similarly situated regions, and cost data, this paper examines three critical veterinary public health issues affecting the YK Region: (1) human exposures to rabies, (2) dog bite injuries and dog overpopulation, and (3) how a veterinary preventative medicine program like HOP can provide a cost-effective way to improve human health and reduce the cost to individuals and the community from these risks. The HOP model demonstrates effective and consistent veterinary preventative care in that it increased the number of vaccinated dogs, thereby reducing rabies exposure risk to humans. As demonstrated in this paper, the authors believe the education and population control provided by programs like HOP can decrease the number of dog bites and dog overpopulation. The authors strongly advocate for the implementation of this One Health model of veterinary service within the established human health system to maintain consistency and effectiveness.
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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 | 0.057 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.022 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.011 | 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".