Reflections on the provision of veterinary services to underserved regions: A case example using northern Manitoba, Canada.
Bibliographic record
Abstract
Rural, remote, and Indigenous communities often contend with free-roaming dog populations, increasing the risk of aggressive dog encounters, particularly dog bites and fatal dog attacks. This qualitative survey gathered a range of perspectives to ascertain the current veterinary services available in rural, remote, and Indigenous communities of northern Manitoba, as well as needs, barriers to, and considerations for future veterinary care provision. Survey results indicated terminology such as "overpopulation" and "rescue" need to be carefully considered as they may have negative connotations for communities. While veterinary services such as vaccination and deworming are important for public health, most programs were focused on sterilization. There was consensus that conversations must begin with individual communities to determine what services are needed and how to fulfil those needs. Perceived barriers include the remoteness of communities, finances, and culturally different views of veterinary medicine. Recommendations for future delivery of services include increased frequency and funding of current models, while others focused on different methods of delivery; all of which will require further discussions within the veterinary community and with other stakeholders.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.034 | 0.007 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".