Public policy and veterinary medicine.
Bibliographic record
Abstract
Public policy for human, animal, and ecosystem health has a profound influence on veterinary medicine. The primary role of veterinarians employed by government is to help formulate, deliver, enforce, and evaluate programs pertinent to public policy; an activity the profession calls public practice. However, all veterinarians in the private sector and academia are at times engaged in activities that contribute to and shape public policies. Given the seminal importance of public policy it is worrisome that the veterinary profession does not give more focused attention to this domain. A strong case can be made that there is a need for more veterinary expertise and perspective in policy making at multiple levels and in both Government and non-Government settings. Fortunately our Canadian profession could muster a sufficient resource of public, private, and academic practitioners to effectively deal with this issue. Health policy is amenable to study, research, and enhancement in the same way as other medically related disciplines that have emerged in professional practice and academia. Health-related public policy is akin to clinical medicine in that it draws on many disciplines in its formulation and application. Policy merits the same degree of specific attention accorded to other discrete fields of practice through professional organizations and educational curricula. At present no veterinary professional organization espouses expertise in public policy as a basis for existence. Who better than an organization of veterinarians in public service to fill this void? At this point in the evolution of veterinary medicine there is widespread support for the concept of One-Health in dealing with inter-connected issues and phenomena in promoting human, animal, and ecosystem health. Since health includes the capacity for achieving reasonable human goals, its parameters are ultimately reflected in public policies of the day. Hence the full benefits of One-Health will ultimately depend on influencing public policy. Therefore it is timely for the profession to make a concerted effort to put more emphasis on acquiring expertise directly related to policy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.015 | 0.008 |
| Insufficient payload (model declined to judge) | 0.090 | 0.019 |
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".