Resilience in Veterinary Students and the Predictive Role of Mindfulness and Self-Compassion
Bibliographic record
Abstract
Resilience is a dynamic and multifaceted process in which individuals draw on personal and contextual resources. In difficult situations, resilient people use specific strategies to learn from the situation without being overcome by it. As stressors are inherent to veterinary work, including long work hours, ethical dilemmas, and challenging interactions with clients, resilience is an important component of professional quality of life. However, while resilience in other health professionals has received attention, it has received little in the veterinary field. In this cross-sectional study, veterinary students from six veterinary schools in Australia completed an online survey, with 193 responses (23%). Very few veterinary students (6%) reached the threshold to be considered highly resilient using the Brief Resilience Scale, and approximately one third classified as having low levels of resilience. In the final linear multiple regression model, predictors of resilience included nonjudgmental and nonreactive mindfulness (Five Facet Mindfulness Questionnaire) and self-compassion (Neff Self-Compassion Scale). Students with higher nonjudgmental and nonreactive mindfulness and self-compassion had higher resilience scores. These findings indicate that fostering these qualities of mindfulness and self-compassion may be aligned with strengthening veterinary student resilience. Importantly, if the factors that help veterinary students develop a capacity for resilience can be identified, intervention programs can be targeted to educate future veterinary professionals with a high quality of life, both professional and personal.
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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 | 0.017 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".