Teaching Biostatistics and Epidemiology in the Veterinary Curriculum: What Do Our Fellow Lecturers Expect?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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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 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it