Impact of a Frame Reflection Assignment on Veterinary Student Perspectives toward Animal Welfare and Differing Viewpoints
Bibliographic record
Abstract
Veterinarians are considered leaders in animal welfare, but veterinary curricula often lack training in welfare. Our aims were to describe veterinary student values, assess whether a frame reflection assignment can encourage student willingness to engage with others with differing values surrounding animal welfare, and determine if sex and career area of emphasis related to responses. Two cohorts of second-year veterinary students at the Ohio State University ( n = 314) articulated their values (including those of biological functioning, affective states or natural living they value most), interviewed someone with differing values, reported their interviewee’s views as if they were their own (frame reflection), then reflected on the process (post-interview reflection). Qualitative and quantitative analyses were used. Students identified as 83% female and 17% male, a with small animal (56%), large animal (11%), mixed animal (15%), or other (19%) career areas of emphasis. Students valued affective states (45%) and biological functioning (42%) more than natural living (11%). Food animal students were more likely to value biological functioning. In their post-interview reflections, students articulated both productive and unproductive views (e.g., likely or unlikely to improve future conversations, respectively), though productive views were more common. Students reported that the assignment would benefit their careers by improving their communication strategies with clients. Female students were more likely to use themes related to unproductive and career-related views. We conclude that a frame reflection assignment is a novel and effective method for improving veterinary student communication skills when discussing controversial animal welfare topics.
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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 | 0.040 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".