First-Year Veterinary Student Perspectives from One Institution on Elements Contributing to Career Satisfaction: A Longitudinal Analysis from 2016 through 2023 Including Pre- and Post-COVID Comparisons
Bibliographic record
Abstract
To determine individual student viewpoints on elements important for career satisfaction in our institution, we directly surveyed first-semester veterinary students and collated their responses. For this study, we asked first-year veterinary students in the initial (fall) semester of their curriculum to identify elements of veterinary employment that they view as important to their career satisfaction in a veterinary job. Using a Qualtrics survey instrument of directed questioning, students rated designated future career elements from "Not important" to "Very important." Students were surveyed at the start of fall of 2016 (Class of 2020) through fall of 2023 (Class of 2027) and responses were compared between classes (years) to determine early career veterinary student perspectives over time. The (null) hypothesis was that there would be no statistically significant change in the relative importance of these elements over the study period. This hypothesis was accepted for some items (no change in rating over time examples: "Feeling pride in my work," "Being competent in my skills," and "A safe work environment") and rejected for other items (change in rating over time examples: "Salary," "Having my weekends free," "Having a four-day work week," "Flexible work hours," and "Adequate staffing"). Comparative results suggest that relative ratings of importance have increased with elements such as salary, personal time-related activities, and staffing levels. These results, especially if representative of the global veterinary student population, may influence both formal and informal educational discussions relative to career success topics throughout the curriculum as these students are preparing to enter the veterinary profession.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".