Training Method and Other Factors Affecting Student Accuracy in Bovine Pregnancy Diagnosis
Bibliographic record
Abstract
To optimize bovine pregnancy diagnosis (PD) training, factors influencing student performance were investigated. The objective was to determine whether training method, gender, background (farm, urban, or mixed), previous experience in bovine PD, and current career interest influenced the accuracy of bovine PD by trans-rectal palpation (TRP). Fourth-year (of a 6-year program) veterinary students (n=138) received one PD training session in groups using either simulator training on Breed'n Betsy (BB) or training on live cows (C). Students completed a questionnaire on gender, background, and career interest. Students' PD accuracy (pregnancy status and stage) was determined after training when each student palpated six cows with known pregnancy status. Students' accuracy in determining pregnancy status was measured as sensitivity and specificity (the ability to correctly identify the presence and absence of pregnancy respectively). Factors that influenced overall accuracy with a higher student sensitivity of bovine PD by TRP were training method, farming background, an interest in a mixed animal career, and stage of gestation. Gender of students and previous experience in bovine PD did not have an influence. Training on BB simulators was associated with lower student sensitivity for pregnancy detection in cows <6 months pregnant. Student sensitivity for pregnancy detection in cows >6 months pregnant was similar for training on BB simulators and live cows. No evaluated factors were significantly associated with specificity of PD. Teaching efforts focusing on specificity of PD and repeated simulator-based training in conjunction with live cow exposure are recommended.
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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 | 0.002 | 0.013 |
| 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 | 0.001 |
| Open science | 0.000 | 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".