Using Strategies from Physical Training of Athletes to Develop Self-Study Programs for Veterinary Medical Students
Bibliographic record
Abstract
Veterinary medical students, similar to elite collegiate athletes, are developing strategies for learning new skills and for self-care to take their performance to the next level. As veterinary students learn to successfully navigate an information-dense, high-volume curriculum, many sacrifice wellness, leadership opportunities, extracurricular activities, and social interactions. Strategies from athletes' physical training were used to design a self-study program for first-year veterinary medical students. Major considerations in program design were the characteristics of the human being, learning goals, and contextual constraints. The study program included a warm-up, study sessions, and a cooldown. The program was offered to first-year veterinary medical students at Purdue University's College of Veterinary Medicine. Thirty-two students requested study programs and 21 completed surveys at the semester end. Results were analyzed quantitatively and by using an adapted conventional content analysis approach. Responses were organized into three main domains: reason for participation, program utility, and program satisfaction. Students shared that the most helpful aspects of the program were assisting with organization and time management, providing accountability, and reducing overwhelm by enhancing well-being and performance; they reported that these learned skills would support their well-being as future professionals. This article describes the experiences of one group of veterinary students at one college using these programs. The long-term goal is to develop a model program for all veterinary students to manage curricular demands while maintaining well-being.
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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 | 0.002 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".