Mandatory Continuing Veterinary Medical Education Requirements in the United States and Canada
Bibliographic record
Abstract
trend is similar in veterinary medicine, except that the primary mechanism used is to require a specified number of continuing veterinary medical education (CVME) credits as qualifications for re-licensure. Some states add the additional restriction of accepting credits only from courses approved by the licensing authority directly or through a designated third-party evaluator. Methods of validating claimed CVME range from the honor system, where the veterinarian is required to swear that s/he participated in the courses claimed, to a system of documentation, where attendance certificates, provided by the CVME sponsoring organization, are required for each CVME event claimed. While approval of CVME activities is determined by licensing authorities and may be scrutinized on an item-by-item basis, many states have provisions for organizations that regularly develop, produce, and deliver CVME courses, seminars, and programs (called providers), with the result that all offerings from these providers are automatically approved for credit. Frequently, recognized approved providers include AVMA accredited schools and colleges of veterinary medicine, national veterinary associations and specialty colleges, state and local veterinary medical associations, respected regional and private providers, and industry. The purpose of this article is to list the current CVME requirements in the United States and Canada and provide additional analysis and comment on CVME requirements. METHODS
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".