Insights into the Future Generation of Veterinarians: Perspectives Gained From the 13- and 14-Year-Olds Who Attended Michigan State University’s Veterinary Camp, and Conclusions about Our Obligations
Bibliographic record
Abstract
Veterinary medicine is at a crossroads: the future of the profession will be determined by those who join it and by those who select who will join it. Veterinary schools are the gatekeepers of the profession, and the entire veterinary profession is responsible for ensuring that the image it presents to those who will join it matches the social needs that it must serve. The application process for a Michigan State University College of Veterinary Medicine (MSUCVM) academic summer camp provided an opportunity to discern attributes of the 314 eighth-grade students who attended in 2000-2002. A re-reading of their application essays allowed clustering of similar descriptions and comments about motivations to attend the camp, interests in science, interactions with animals, and exposure to veterinarians and veterinary medicine. Many veterinary camp attendees will be undergraduate students by 2005/2006 and will be applying to colleges of veterinary medicine between 2008 and 2010. There-fore, an understanding of their attributes is germane to discussions about desirable characteristics of veterinary college applicants. Although the camp was designed to attract eighth graders interested in science and curious about veterinary medicine, attendees frequently described veterinary medicine as their career goal. These students (89.5% female, 95.6% residents of Michigan) enjoyed science, but their interest in veterinary medicine related to emotions such as a love of animals and sympathy for sick or injured animals (96.1%). They discussed having pets in their homes (75.5%), involvement with horseback riding (20.7%), experiences with animal-related projects and activities in 4-H (17.2%), and husbandry experience at farms or stables (16.2%). Although 22.6% had already shadowed a veterinarian and 12.8% described receiving other forms of veterinary mentoring, 22.9% commented on their inability to gain shadowing exposures prior to age 16. Based on the results of this survey and years of working with adolescents interested in veterinary medicine, the author offers conclusions about mentoring youth with an interest in veterinary careers.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".