Analysis of Short-Answer Question Styles versus Gender in Pre-Clinical Veterinary Education
Bibliographic record
Abstract
One large study in medical education has reported that the choice of question format (or question content) could introduce a gender bias, with men outperforming women on questions with a true-false component or that required knowledge of anatomy or physiology. The purpose of our study was to ascertain whether this finding is also true in veterinary medical education. Two veterinary student cohorts were analyzed across four different modules over a three-year period (804 questions in total). The results of the study show that the women's and men's performance did not differ in any of the question types analyzed across any module or year. When students' (both women and men) overall average performance on different question types was compared with their performance on standard multiple-choice questions (MCQs), performance levels increased when students were asked to answer MCQs that contained an image-based prop (IMCQ) such as a photograph, X-ray image, or diagram. In contrast, students' performance was consistently lower when answering assertion-reason questions (ARQs), and this performance could not be explained by the demographic makeup of the two cohorts analyzed. When comparing standard MCQs with MCQs that contained a true-false question stem, no specific trend in the data could be determined. In conclusion, this study suggests that the short-answer question style does not bias against one gender in veterinary medical education, but that overall students do perform differently according to question type and, in particular, less well when ARQs are used in examinations.
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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 | 0.014 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".