Using Alumni Research to Assess a Veterinary Curriculum and Alumni Employment and Reward Patterns
Bibliographic record
Abstract
RATIONALE FOR THE STUDY: The purpose of the study was to obtain an outcomes assessment of the professional degree program of a veterinary college and to determine whether recently published national gender differences were true for a program that has always been predominantly female. METHODOLOGY: A survey was developed and mailed to all alumni of the veterinary degree program at North Carolina State University. Anonymous responses were collected by an independent organization for summation. Results were expressed numerically where possible, although the survey included numerous opportunities for textual responses. Responses were stratified by year of graduation, in five-year increments, and by gender of the respondents. RESULTS: Sixty-one percent of graduates remained within North Carolina. While most of the prerequisite courses were considered useful preparation for the curriculum, physics and calculus were exceptions. Over half the alumni thought time allocations to specific courses were appropriate. However, there were substantial differences between courses. The alumni were satisfied with most aspects of the training program, although there were differences between various components. There were substantial gender differences in length of first employment, salaries, species treated, practice ownership and type of ownership, and career satisfaction. Most alumni work in small animal private clinical practice. Ninety-four percent were employed within three months of graduating. Ninety-five percent did no work with the state's predominant agricultural species. Nineteen percent of alumni were either unsure, probably would not, or definitely would not become a veterinarian if they could choose again. CONCLUSION: The survey was a tremendously valuable source of information. Results provided support for curricular revision. The survey also provided comparative data in relation to national norms, where such norms were available. Unfortunately, few reports of this type are available, making inter-institutional comparisons difficult. The Association of American Veterinary Medical Colleges could assist with standardizing the process of outcomes assessment.
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 | 0.032 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".