Veterinary Students' Perspectives on Resilience and Resilience-Building Strategies
Bibliographic record
Abstract
In recent years, resilience has been lauded as a valuable, even necessary, facet of an effective veterinary practitioner. This study describes a mixed-methods research exploration of the impact of a self-care and mental well-being teaching intervention on the self-reported resilience of 105 first-year veterinary students enrolled at the School of Veterinary Medicine, University of Surrey, UK. Quantitative data were obtained through a questionnaire, the 10-item Connor-Davidson Resilience Scale (CD-RISC 10), which students completed before and after the teaching intervention. The median total score on the scale increased from 27 (IQR=25-30) to 29 (IQR=26-32) (p<.001), a medium effect size (r=-0.28). Student focus groups were held to allow qualitative data analysis of the students' perspectives on the teaching intervention and on the topic of resilience in general. The results of this study suggest that appropriate training in resilience-building strategies can help veterinary students build greater awareness of resilience, and potentially support their development of a more resilient approach in their personal and professional lives. In this study, veterinary students felt that resilience training was a valuable addition to the veterinary curriculum, and that resilience likely plays an important role in achieving a successful veterinary career. The study also suggested that veterinary students utilize a variety of different resilience-building strategies, including drawing on past experiences, seeking help from support networks, and developing an ability to change their perspectives.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".