Dairy Reproductive Management: Assessing a Comprehensive Continuing Education Program for Veterinary Practitioners
Bibliographic record
Abstract
Comprehensive continuing veterinary medical education (CVME) programs are critical for veterinary practitioners to update their knowledge and improve their skills and services. CVME must offer an educational environment in which veterinarians can effectively rejuvenate their knowledge and skills and learn about new practices. The Ohio Dairy Health and Management Certificate Program is a comprehensive CVME program for practicing dairy veterinarians that was developed to provide advanced training on previously identified needs of the dairy industry. Our objectives in this article were (1) to provide a description of a comprehensive CVME program designed to enhance the flow of applied, research-based knowledge from educators and researchers to dairy veterinary practitioners and (2) to provide an assessment of outcomes achieved and experiences gained after the delivery of the first two modules on advanced dairy reproductive management. Findings from the two reproductive modules suggested that (1) the designed dairy reproductive management program was able to meet the participants' educational needs, (2) the implemented delivery methods significantly increased participants' knowledge level, and (3) additional educational needs should be addressed with future programming. In conclusion, results from the participants' self-reports suggested that both reproductive modules were relevant and effective, offering new information with immediate field application. These types of educational programs are important for dairy veterinary practitioners because they are a vital source of information and service providers for dairy producers. For the program to be considered completely successful, a detailed follow-up assessment of participants' behavior change, adoption of new practices and skills, and their on-farm impact is needed.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".