Examining need, capacity, and barriers to accessing food animal veterinary services in underserved areas of rural Ontario
Bibliographic record
Abstract
Livestock farms need consistent and reliable access to veterinary services to ensure optimal animal health and welfare, and to maintain food safety. Certain regions of the province, such as northern and to an extent, eastern Ontario, have few/no food animal veterinary clinics, which make it difficult for livestock operations in these areas to receive routine or even emergency veterinary services in a timely manner. With growth in the livestock sector, the need for food animal veterinarians has increased and the challenges for under-serviced communities has heightened. This study identifies under-serviced agricultural communities across Ontario and examines the social and economic barriers that impact food animal veterinarian attraction and retention to these communities. By engaging producers, veterinarians, government staff, and numerous other stakeholders, this project will develop a series of policy, program, and practice recommendations to improve veterinary services to these underserved regions of Ontario. An overview of preliminary research findings gathered from interviews and focus groups conducted with various stakeholders including farmers, and veterinarians and current veterinary students from the Ontario Veterinary College will be explored. Another important component of this project is determining where veterinary shortages exist within rural Ontario. In order to identify under-serviced areas across the province, the location of food animal veterinarians is being mapped. ArcGIS software is being used to map the current geographic location of these facilities in the regions of interest, which will allow us to characterize the number and type of farms by region, and the proximity of veterinary services. Funding: OMAFRA through the Ontario Agri-food Innovation Alliance (Special Initiatives Program)
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".