Relationship between Anticipatory Socialization Experiences and First-Year Veterinary Students' Career Interests
Bibliographic record
Abstract
The purpose of this project was to explore first-year veterinary students' anticipatory socialization-life, education, and social experiences that assist in preparation for professional occupations-and determine what relationship exists between those experiences and career interests. Seventy-three first-year veterinary students enrolled in the Careers in Veterinary Medicine course completed the Veterinary Careers survey. Results show that students' anticipatory vocational socialization experiences are significantly related to their stated career interests. The career interests with the highest percentage of students expressing "a great deal of interest" included specialty private practice (37%), research and teaching in an academic setting (33%), and international veterinary medicine (31%). The career interests with the highest percentage of students expressing "no interest at all" included the military (50%), equine private practice (42%), and the pharmaceutical industry (41%). Less than half of the students (42%) stated that they reconsidered their career path after the first semester of veterinary school, but the majority (87%) developed a better understanding of how to pursue a nontraditional career path should they choose to do so.
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 | 0.002 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".