Monitoring the Veterinary Medical Student Experience: An Institutional Pilot Study
Bibliographic record
Abstract
Veterinary medical school challenges students academically and personally, and some students report depression and anxiety at rates higher than the general population and other medical students. This study describes changes in veterinary medical student self-esteem (SE) over four years of professional education, attending to differences between high and low SE students and the characteristics specific to low SE veterinary medical students. The study population was students enrolled at the Michigan State University College of Veterinary Medicine from 2006 to 2012. We used data from the annual anonymous survey administered college-wide that is used to monitor the curriculum and learning environment. The survey asked respondents to rate their knowledge and skill development, learning environment, perceptions of stress, skill development, and SE. Participants also provided information on their academic performance and demographics. A contrasting groups design was used: high and low SE students were compared using logistic regression to identify factors associated with low SE. A total of 1,653 respondents met inclusion criteria: 789 low SE and 864 high SE students. The proportion of high and low SE students varied over time, with the greatest proportion of low SE students during the second-year of the program. Perceived stress was associated with low SE, whereas perceived supportive learning environment and skill development were associated with high SE. These data have provided impetus for curricular and learning environment changes to enhance student support. They also provide guidance for additional research to better understand various student academic trajectories and their implications for success.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".