Beyond clinical skills: student-reported impacts of a veterinary public health externship in rural Alaska
Bibliographic record
Abstract
Preventive medicine and public health are critical components of veterinary curricula, requiring students to understand their role in the health of communities and people beyond their animal patients. However, students considering a career in public health often face gaps in the depth of this curriculum. From 2019 to 2024, students from Colorado State University (CSU) and its University of Alaska Fairbanks (UAF) partner participated in the Hub Outpost Project (HOP) externship's community visits to Yukon-Kuskokwim Delta communities in rural Alaska. The curriculum emphasized veterinary professional concepts, surgical and clinical experience, community engagement and cultural awareness, and self- and team- mindfulness. Through repeated practice and direct exposure to providing care, students improved a variety of clinical and professional skills. Student impacts were assessed through a survey of the participating veterinary students from both institutions. Students primarily reported impacts related to gaining clinical and communication skills, the breadth of human-animal bonds, engaging directly with clients and patients, and realizations and understandings that quality medicine is possible with limited resources. These findings suggest that experiential learning not only greatly improves skills for students but also engages students in areas of veterinary medicine that need an increased workforce, such as rural medicine, while increasing their understanding of diverse community needs.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".