Teaching Biostatistics and Epidemiology in the Veterinary Curriculum: What Do Our Fellow Lecturers Expect?
Bibliographic record
Abstract
Given veterinary students' varying mathematical knowledge and interest in statistics, teaching statistical concepts to them is often seen as a challenge. Consequently, there is an ongoing debate among lecturers about the best time to introduce the material into the curriculum, and the best thematic content and conceptual approach to teaching in basic biostatistics classes. During a workshop meeting of epidemiology and biostatistics lecturers of Austrian, German, and Swiss veterinary schools, the question was raised as to whether the topics taught in epidemiology and statistics classes are of sufficient relevance to our lecturing colleagues in other fields of veterinary education (i.e., whether our colleagues have certain expectations as to what the students should know about biostatistics before taking their classes). In 2012, an online survey was compiled and carried out at all eight German-speaking veterinary schools to address this issue. There were 266 respondents out of approximately 800 contacted lecturers from all schools and disciplines. Almost 50% responded that the basic biostatistics class should be taught early on (in the second or third year), while only 26% indicated that basic epidemiology should commence before the third year of the veterinary curriculum. There were clear differences in perceived relevance of the 44 epidemiological and biostatistical topics presented in the survey, assessed on a Likert scale from 0 (no relevance) to 4 (very high relevance). The results provide important information about how to revise the content of epidemiology and biostatistics classes, and the approach could also be used for other courses within the veterinary curriculum with a natural science focus.
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 | 0.036 | 0.102 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".