Equine-Assisted Learning—An Experiential, Facilitated Learning Model for Development of Professional Skills and Resiliency in Veterinary Students
Bibliographic record
Abstract
Stress has been identified as a major obstacle for students in DVM training programs and can be associated with a high incidence of anxiety and depression among students. Interventions for stress reduction and improved self-confidence have been introduced at many veterinary schools with the intention of increasing resiliency among students and improving skills for wellness to be used throughout a veterinary career. Equine-assisted learning (EAL) is a facilitated, reflective discussion method based on interpretation of equine behavior in a group experiential setting that has been used to improve confidence, self-assurance, verbal and nonverbal communication, focus, mindfulness, and coping strategies in populations of students, medical students, corporate groups, and career professionals. We worked with the Cummings School equine teaching herd to develop an EAL course offered as a weekly class to veterinary students in spring and fall semesters since 2018. Our course was modeled after one offered to medical students at the University of Arizona and Stanford University, using progressively more complex equine handling exercises over the course of the semester. Our goal was improved communication, focus, and self-awareness among students to help reduce stress and improve resiliency. Outcome surveys showed that the students found a safe space to share anxieties, concerns, and challenges and learn professionalism skills. Incidentally, they also reported improvement in their equine handling skills. We advocate the use of EAL principles and the use of veterinary teaching horses to reduce stress and improve resiliency and equine handling skills among veterinary students.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".