An Alumni Survey to Assess Self-Reported Career Preparation Attained at a US Veterinary School
Bibliographic record
Abstract
The American Veterinary Medical Association (AVMA) Council on Education (COE) has challenged veterinary schools to improve self-assessment of curricular outcomes. One way to assess the quality of education is to gather feedback from alumni. To successfully gather feedback using a questionnaire, questions must be pertinent to veterinary education and include quantifiable responses. Several principles must be applied in questionnaire development to ensure that the questions address the intended issues, that questions are interpreted correctly and consistently, and that responses are quantifiable. The objectives of the questionnaire for alumni of Mississippi State University's College of Veterinary Medicine (MSU-CVM) were twofold: (1) to determine whether graduates were comparable to their US peers in terms of work opportunities and salary, and (2) to evaluate how well the CVM curriculum prepared students to begin their veterinary careers. Demographic categories used by the AVMA and published knowledge, skills, attitudes, and aptitudes of veterinary graduates were used in developing the questions. College-specific questions, such as those relating to student activities and impressions of college resources, were also incorporated. Questionnaires were mailed to participants, who could respond via the World Wide Web. Questionnaire results allowed leaders within the college to determine which aspects of alumni's experiences were exceptionally positive, which needed immediate response, and which might require further study. This article describes the application of principles in developing, administering, and analyzing responses to a questionnaire regarding veterinary education.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".