Factors Influencing the Choice of a Career in Food-Animal Practice among Recent Graduates and Current Students of Texas A&M University, College of Veterinary Medicine
Bibliographic record
Abstract
Concerns about a shortage of large and mixed-animal veterinarians have been discussed in the profession. To better understand veterinary career choices among currently enrolled veterinary students (classes of 2007-2010) and recent graduate veterinarians in Texas (classes of 2002-2006), an online survey was developed. The objectives were to examine: (1) the respondents' backgrounds, demographic data, and experiences; (2) the respondents' working conditions and rural lifestyle considerations; (3) the respondents' perceptions of large/mixed-animal practice; and (4) the factors that have influenced respondents' career choices. The response rate was 37% (390/1,042). Overall, 72% of students and 55% of recent graduates were interested in large/mixed-animal practice. More than 70% of respondents indicated that veterinary practitioners had the strongest personal influence on career choices. Respondents who were no longer interested in large/mixed-animal practice, or who had never been interested, reported no experience with large animals (42% and 64%, respectively) as the most common reason for their lack of interest. Previous and current interest in large/mixed-animal practice were associated with working in a large/mixed-animal practice, any agricultural experience, and working for at least 6 months on a farm or ranch. Any 4-H experience increased the likelihood of previous interest, while being married decreased the likelihood of current interest. Student contact with practitioners (82%) and financial considerations (77%) were most commonly cited as factors that would make a career in large/mixed-animal practice more attractive. Rural lifestyle drawbacks influenced respondents' career choices. Many forms of agricultural experience may expose and encourage students to consider large/mixed-animal practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".