Animal-Assisted Mental Health Education for Veterinary Students
Bibliographic record
Abstract
Veterinary students often face mental health challenges due to the demanding nature of their studies and the pressures of adapting to their future profession. To address this issue, an animal-assisted education in mental health (AAE-MH) program was developed and implemented at a veterinary school in Hong Kong. The primary goal of the AAE-MH program was to enhance students' mental health literacy, raise awareness of mental health topics, and improve their help-seeking behavior and overall well-being. By leveraging the students' natural affinity for animals, the program incorporated a blend of course-based and experiential learning activities to tackle the often-taboo topic of mental health in the veterinary field. The AAE-MH program consisted of six sessions, each lasting 1 hour and 50 minutes. Two of these sessions involved 1 hour of learning from certified therapy dogs. This interdisciplinary program drew expertise from veterinary mental health professionals, psychologists, animal-assisted therapists, and veterinary school faculty. This collaborative effort ensured that the program covered both the biomedical and humanistic aspects of veterinary medicine, preparing students to better understand and support their own and their peers' mental well-being. The AAE-MH program was conducted during the COVID-19 pandemic, and appropriate precautionary measures were taken. This teaching tip outlines the key elements of the program, including the course design, delivery, and evaluation of its effectiveness. We hope that this framework can provide fellow educators with the opportunity to potentially adapt and implement similar initiatives within their own veterinary education contexts, ultimately benefiting the entire veterinary profession.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".