Teaching the Principles of Health Management to First-Year Veterinary Students
Bibliographic record
Abstract
A course called Health Management 1 was created as part of a new DVM curriculum at the Ontario Veterinary College. This full year course was designed to introduce students to basic concepts of health management, integrating the disciplines of epidemiology, ethology, and public health in the context of selected animal industries. The course was comprised of 60 lecture hours and four two-hour laboratories. A common definition of health management, incorporating five principles, was used throughout the course, in order to reinforce the concepts and to maintain continuity between lecture blocks. Unlike in the years prior to the introduction of the new curriculum, epidemiology was presented as a tool of health management rather than as a separate discipline. To supplement the lecture and laboratory material, a Web-based resource was created and the students were required to review the appropriate section prior to each lecture block. Small quizzes, consisting of 10 questions each within WebCT, were used to stimulate self-directed learning. Overall, the course was well received by the students. The Web resources combined with the WebCT quizzes proved to be an effective method of stimulating students to prepare for lecture.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".