Integrating Individual Student Advising into Financial Education to Optimize Financial Literacy in Veterinary Students
Bibliographic record
Abstract
The debt-to-income ratio (DIR) of Doctor of Veterinary Medicine (DVM) students has exceeded the recommended 1.4 and it is predicted that the DIR will approach 2.18 by 2026. The associated stressors negatively impact professional satisfaction and well-being. In conventional approaches to financial education, content is delivered to groups of students as part of the curriculum, but with little opportunity for application. Research in medical and financial education suggests that convenient timing, relevant subject matter and individualization are key characteristics of a successful program that promotes retention and application of knowledge. In this article, we describe an integrative approach to financial education developed by the Colorado State University (CSU) Financial Education Specialist (FES). The FES position requires that the individual be qualified to provide one-on-one financial advising to DVM students as well as develop targeted curricular interventions and optional workshops. Data from student and alumni surveys suggest that this integrative approach to financial education both improves knowledge and alters behaviors surrounding financial management. Interest from academic and professional entities across the United States reflects recognition of the program as an emerging best practice. We describe lessons learned through program implementation, including demands for FES services throughout the academic year, and topics relevant to each student cohort. We propose that providing one-on-one financial advice to DVM students is a critical component of a broader financial education program. Actualizing timing, relevance, and individualization, this integrated approach optimizes opportunities for knowledge application and ultimately behavioral change.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".