Community perspectives on dogs, health risks, and veterinary care impacts in rural Alaska
Bibliographic record
Abstract
Veterinary care in much of Alaska is extremely limited due to the high cost of living and low access to care. To date, community perspectives on veterinary care and the efficacy of veterinary services introduced have not been documented in Alaska Native communities, in this case specifically the Yukon Kuskokwim (YK) Delta of Alaska. This stymies the introduction of thoughtful public policy created with community insights. It also impedes program funding and awareness of the public health importance of veterinary care. This study aims to utilize information from surveys to form a basis in literature for veterinary medicine in rural Alaska and to document community perspective input to aid in making veterinary care sustainable in this region. Using information gathered from 390 surveys completed in 25 YK Delta communities between 2019-2024, The authors found general support for dogs being a part of the community and culture in YK Delta communities. Specifically, nearly all respondents like dogs (95%), 94% of respondents reported owning animals, and dogs are the most common type of animal owned or cared for in this region by far (92%). Yet community members feel that stray and unwanted dogs are a problem (71%) and loose dogs are feared (69%). Respondents also report that dogs spread waste and garbage around their community (75%). Perceptions of and actual ability to access veterinary care are low. Community members report dogs have low rates of vaccination (a reported 62% rate of rabies vaccination) and low sterilization rates (53% reported rate of sterilization surgeries completed). Consequences of overpopulation noted by respondents also include dog bites and attacks, and negative impacts of having to remove unwanted dogs from the community. Recommendations are given on actions to take on the federal, state, city, and village levels to increase access to veterinary care based on these findings. Increasing awareness of the current state of veterinary care and the potential benefits to humans and animals is a key step to improving access to care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| 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".