Characteristics of Veterinary Students: Perfectionism, Personality Factors, and Resilience
Bibliographic record
Abstract
Perfectionism is a topic relevant to veterinary medicine and has previously been found to be related to higher levels of stress and poorer mental health outcomes. However, many aspects of perfectionism have yet to be researched among veterinary students. This research investigates the relationship between perfectionism and the "Big Five" personality factors. Additionally, the relationship between resilience and neuroticism is addressed. This research includes a sample of 99 veterinary students enrolled at a College of Veterinary Medicine in the southeastern United States. Students completed the Multidimensional Perfectionism Inventory (MPI), the Big Five Inventory (BFI), and the Brief Resilience Scale (BRS). Results show that perfectionism is significantly correlated with personality factors; specifically, self-oriented perfectionism and socially prescribed perfectionism are associated with neuroticism, socially prescribed perfectionism is associated with agreeableness, and self-oriented perfectionism is associated with conscientiousness. Neuroticism was found to have a significant negative correlation with resilience. Findings indicate that veterinary mental health professionals and educators should consider implementing specific strategies to help students develop a healthy balance in their perfectionistic beliefs and have targeted interventions to promote student resilience.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".