Career Attitudes of First-Year Veterinary Students Before and After a Required Course on Veterinary Careers
Bibliographic record
Abstract
Careers in Veterinary Medicine is a required, one-credit-hour course at the North Carolina State University College of Veterinary Medicine (NCSU-CVM), which meets once weekly during veterinary students' first semester. Lectures in this course are presented by one or more veterinarians representing diverse career areas. A voluntary, anonymous survey was distributed before the first class meeting in 2011 (PRE) and at the end of the semester (POST) to assess if students' career interests changed during the semester. The survey collected basic demographic data and students' preferences (on a Likert scale) for 17 veterinary career paths. Out of 63 students, 36 (57%) in the POST survey said that their career interests had changed during the semester, and 17 of the 26 students (65%) who gave a reason credited the careers course as one factor in reconsidering their career choice. Only 3 of the 17 career paths had statistically significant PRE/POST survey differences in Likert response frequency (equine practice, pathology, and wildlife medicine), but both informal discussions with students and responses to open-ended survey questions indicated that many students valued the introduction to unfamiliar veterinary career areas. Careers in Veterinary Medicine is a vital component of recent career-planning initiatives in the college, which will be especially important to veterinary students as they face continued changes in the profession, such as the increased debt load of the new graduate and the threat of veterinary workforce oversupply.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".