Exploring bison producers' access to veterinary services in Ontario, Canada
Bibliographic record
Abstract
Introduction: Access to veterinary services is integral for animals of all species. These services play a crucial role in maintaining their health and welfare and maintaining a healthy, safe, and sustainable food system. Research has consistently shown that rural communities face challenges accessing veterinary services, with livestock producers outlining several barriers including cost, inadequate infrastructure, and delays in receiving treatments. Research on bison producers' access to veterinary services is limited, prompting our investigation to address this gap in knowledge. This qualitative study aimed to describe Ontario bison producers' current access to veterinary services and identify how any barriers, as perceived by producers, might impact their herd health and management practices. Methods: Ontario bison producers were invited to participate in virtual focus groups to share their perspectives on their access to veterinary services. Audio from the focus groups was recorded, transcribed verbatim, and analyzed using reflexive thematic analysis. Results: Despite all participants indicating they had access to veterinary services, they also encountered obstacles and expressed concerns accessing and utilizing these services. Two overarching themes were identified: producers were concerned about the future stability and costs associated with bison farming, and they had a desire to improve bison-specific knowledge among veterinarians servicing their farms. Producers suggested several strategies to address these concerns, including improving collaborations with veterinary organizations, like the College of Veterinarians of Ontario, to increase veterinarians' exposure to bison, building stronger relationships between producers and their veterinarians, monetary incentives for established food animal veterinarians, and providing financial support to prospective food animal veterinarians. Discussion: The findings of this study demonstrate that although bison producers in our sample had access to veterinarians, they may not fully utilize the services or find their access entirely beneficial. Future research into the veterinarian perspective would allow for greater insights into these barriers, adding additional value and contributing to a more wholistic understanding of the topic.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".