Exploring Valued Personality Traits in Practicing Veterinarians
Bibliographic record
Abstract
We examined differences in valued Big Five personality traits of small animal veterinarians between members and nonmembers of the veterinary medicine community. Between fall 2019 and spring 2020, data were collected from an online survey sent to eligible persons across a US midwestern state. Eligible persons included veterinary office clients (i.e., pet owners) and persons practicing/training in veterinary medicine. Participants completed demographic questions and 10 Likert scale items about which Big Five personality characteristics they prefer in a veterinarian. Descriptive data were determined and checked for assumptions of linearity and normality. Data for the primary analyses were analyzed using Spearman’s correlations and Kruskal–Wallis H tests. Participants who were members of the veterinary community of practice valued the characteristic openness more than clients but valued emotional stability less than clients. Moreover, tests revealed that young adults (aged 18–24) valued extraversion more than all other age groups but least valued agreeableness. Last, participants aged 55 and older valued agreeableness and emotional stability more than the 18–44 age groups. Findings indicate individuals from different membership and age groups have varying preferences in what personality traits they expect in a veterinarian. Clients care more about their veterinarian being able to handle adversity. Older adults want their veterinarian to be trusting and creative. These findings encourage veterinary medical education to spotlight the development of skills congruent with these desired personality traits. Gaining such skills will be useful for veterinarians who seek to grow or build lasting relationships with clientele and colleagues.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".