Improve your management with your complimentary CVMA practice diagnostic report.
Bibliographic record
Abstract
The results of the provincial Economic Surveys are available and members of the Canadian Veterinary Medical Association from across Canada are discovering how their practice compares with those of their provincial colleagues. One of the many benefits for respondents is a personal Practice Diagnostic Report, which compares individual hospital production, scheduling, staffing, finances, and fees to the average and top performing hospitals in their province. This information is critical for hospital owners and managers to evaluate hospital management and performance. Dr. Clayton MacKay from Hill’s Pet Nutrition Canada Inc. (left) with CVMA President, Dr. Paul Boutet. Hill’s is a sponsor of the CVMA Business Management Program. The following excerpts are from the Practice Diagnostic Reports of different provinces. Each provincial report compares companion animal and mixed animal practices separately. Provinces having appropriate responses from equine and food producing animal hospitals will receive species-specific reports in addition. To account for differences in the number of veterinarians (DVMs) per hospital, much of the material is presented as a per full-time equivalent (FTE) DVM. An FTE represents 2000 h of veterinary time. To determine the number of FTE DVMs in your practice, add all of the veterinary hours and divide by 2000. For example, 1 veterinarian working 2000 h/y would equal 1 FTE DVM. If 2 veterinarians worked 1500 h/y each and another 2 veterinarians in the same practice worked 500 h/y each, then the number of FTEs would be (1500 + 1500 + 500 + 500) ÷ 2000 h = 2 FTE DVMs.
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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 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.326 | 0.230 |
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".