A Focused Ethnographic Study of Alberta Cattle Veterinarians’ Decision Making about Diagnostic Laboratory Submissions and Perceptions of Surveillance Programs
Bibliographic record
Abstract
The animal and public health communities need to address the challenge posed by zoonotic emerging infectious diseases. To minimize the impacts of future events, animal disease surveillance will need to enable prompt event detection and response. Diagnostic laboratory-based surveillance systems targeting domestic animals depend in large part on private veterinarians to submit samples from cases to a laboratory. In contexts where pre-diagnostic laboratory surveillance systems have been implemented, this group of veterinarians is often asked to input data. This scenario holds true in Alberta where private cattle veterinarians have been asked to participate in the Alberta Veterinary Surveillance Network-Veterinary Practice Surveillance, a platform to which pre-diagnostic disease and non-disease case data are submitted. Consequently, understanding the factors that influence these veterinarians to submit cases to a laboratory and the complex of factors that affect their participation in surveillance programs is foundational to interpreting disease patterns reported by laboratories and engaging veterinarians in surveillance. A focused ethnographic study was conducted with ten cattle veterinarians in Alberta. Individual in-depth interviews with participants were recorded and transcribed to enable thematic analysis. Laboratory submissions were biased toward outbreaks of unknown cause, cases with unusual mortality rates, and issues with potential herd-level implications. Decreasing cattle value and government support for laboratory testing have contributed to fewer submissions over time. Participants were willing participants in surveillance, though government support and collaboration were necessary. Changes in the beef industry and veterinary profession, as well as cattle producers themselves, present both challenges and opportunities in surveillance.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".