Veterinary Professional Associates: Does the Profession's Foresight Include a Mid-Tier Professional Similar to Physician Assistants?
Bibliographic record
Abstract
The projected shortage of veterinarians has created a need to explore alternatives designed to meet society's future demands. A veterinary professional health care provider, similar to the human medical profession's physician assistant (PA), is one such alternative. To explore this option, this paper provides background information on the development of PAs, including the motivations behind the initiative and the history of the role's development. Rather than aiming for a persuasive appeal, the authors have written this article with the intent of fostering discussion. It is suggested that perhaps veterinary professional associates, modeled after PAs, could be employed to handle routine veterinary care and thereby allow veterinarians additional time to focus on the more demanding and challenging aspects of veterinary medicine. Perhaps a team approach, similar to the physician/PA team, could help the field of veterinary medicine to better serve both clients and patients. As veterinary medicine directs its attention toward the new challenges on the horizon, creative solutions will be needed. Perhaps some variation of a veterinary professional associate is worthy of future discussion.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".