The Motivations and Practice-Area Interests of First-Year French Veterinary Students (2005–2008)
Bibliographic record
Abstract
Concerns about new trends in the veterinary profession in several industrialized countries have received significant attention recently. We conducted an online survey among first-year veterinary students enrolled between 2005 and 2008 in France's four National Veterinary Schools (Ecoles Nationales Vétérinaires [ENV]) to inquire into what determined future graduates' practice-area interests and how they built a concept of their future work. A total of 1,080 students-or 70% of first-year students-responded to the survey. These students were predominantly of middle and higher social classes and most of them lived in urban areas. About 96% of the respondents had made the ENV their first choice when taking the entrance examination. In total, 39.7% declared "vocation" as the leading factor influencing their career choice. Together, the three leading practice types (wild animals, pets, and mixed) contemplated by students after graduating amount to 64.7% of the points awarded. Practice types that are not directly related to animal health were disregarded in this analysis. The results convey both how early and how firmly the choice of the veterinary career is made. They highlight the preponderance of the image of the veterinarian as an "animal doctor," the gap between the respective proportions of practice areas in the current employment pattern of veterinarians, and the aspirations of students upon admission to the ENV. A longitudinal study after one year is needed to test whether or not these career choices change during the five years of the ENV program under the influence of teaching and extramural studies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".