The Impact of Using a Veterinary Medicine Activity Book in the Classroom on Fifth- and Sixth-Grade Students' Depictions of Veterinarians
Bibliographic record
Abstract
Efforts to develop a diverse, future veterinary workforce must start as early as elementary school, when children begin to form perceptions about careers. The objective of the current project was to determine the impact of the Veterinary Medicine Activity Book: Grade 5 on fifth- and sixth-grade students' depictions of veterinarians. The book was delivered as part of the curriculum in four classrooms. Students were asked to draw a veterinarian and describe the veterinarian's activities before and after being exposed to the book. Drawings were evaluated for the gender and race/ethnicity of the illustrated veterinarian, the description of the veterinarian's activity, and animals portrayed. Significant differences were detected within three of four classrooms. In one class, after exposure to the activity book, more students drew male veterinarians and veterinarians performing an activity specifically mentioned in the book. In a second class, more students drew large animals after exposure to the activity book. In a third class, after exposure to the activity book, more students drew large animals and veterinarians performing an activity specifically mentioned in the book. Results provide preliminary evidence that children's depictions of veterinarians can be altered through use of educational materials delivered in classrooms through teacher-led discussion or formal lesson plans.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".