The Impact of COVID-19 on Pre-Veterinary Opportunities and Recommendations for DVM Admissions
Bibliographic record
Abstract
During the summer of 2020, a survey-based study was conducted at North Carolina State University, a land-grant university, to evaluate the impact of COVID-19 on pre-veterinary students' ability to gain experience hours for Doctor of Veterinary Medicine (DVM) admissions. Of the 286 respondents (47% of the respondent pool), 92% reported losing at least one animal, veterinary, research, extracurricular, or work opportunity due to COVID-19, and 59% were not able to find a replacement. Of the lost experiences, 74 (20.8%) were for academic credit, resulting in 131 total academic credit hours lost, while only 12 credit hours were gained via alternative experiences. Of respondents, 30% (29.7%) identified as applicants of the 2020-2021 Veterinary Medical College Application Service (VMCAS) cycle. More than half (52.6%) of the sample identified being concerned about the strength of their VMCAS experiences due to these lost opportunities. Many respondents reported considering delaying application submissions by taking a gap year (17.5%) or having had their intended graduation timeline affected (14.8%). Since the majority of veterinary colleges utilize a holistic review process, this study provides a basis for understanding the effects of COVID-19 on the duration, depth, and diversity of experiences gained by future DVM applicants. This article also provides recommendations for DVM admissions adaptations based on the outcomes of the data.
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 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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".