Annual veterinary productivity in companion animal practice in the different provinces.
Bibliographic record
Abstract
Which province has the highest level of veterinary productivity in companion animal practice? Appointment books, client records, and staff schedules were explored to find out which province is the busiest. The contest was restricted to companion animal practices, because it was felt unfair to challenge veterinary productivity in large animal practices. Most mixed and large animal practices across Canada are operating at below ideal productivity levels because of a drop in demand for food animal services post-bovine spongiform encephalopathy (BSE). There is preliminary evidence suggesting demand is on the rise, but in 2006 that was certainly not the case. Information on veterinary productivity in companion animal practice comes from the 2006 OVMA/CVMA provincial economic surveys, which were undertaken as joint partnerships between the CVMA Business Management Program, individual provinces, and the following corporate sponsors: Hill’s Pet Nutrition Canada, Petplan Insurance, Scotiabank, and Schering-Plough Animal Health (1). The comparison shows the data for all provinces, except Newfoundland where lower than expected response prevented detailed productivity analysis (Table 1). Table 1 Annual veterinary productivity in companion animal practice in the different provinces
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".