Evaluation of Factors Contributing to Veterinary Student Anxiety Prior to Instructional Surgery Laboratories
Bibliographic record
Abstract
Abstract Understanding factors contributing to veterinary student anxiety prior to instructional laboratories is important for mitigating those stressors and improving student education. This study aimed to investigate the relationships between student anxiety prior to surgery and demographic and societal variables before and during the COVID-19 pandemic. We hypothesized that increased county COVID-19 cases would increase anxiety, and experience level would have no impact. Students were enrolled in this study in 2019 ( n = 87), 2021 ( n = 84), and 2022 ( n = 96). Participants completed a demographic questionnaire, the State Trait Anxiety Inventory (STAI), which involved a writing prompt to describe their feelings, and provided a salivary sample immediately prior to their first ovariohysterectomy laboratory. Univariable and multivariable linear models were used to assess for predictors of STAI scores, salivary cortisol levels, and scored prompt responses with significance threshold ( p < 0.05). Year and COVID-19 cases were correlated and considered together. A significant predictor for both STAI-S and STAI-T scores was year/COVID-19 (2019 = 0 COVID-19; STAI-S = 54.7 ± 6.6; 2021 = 679 COVID-19, STAI-S = 67.5 ± 6.6; 2022 = 186 COVID-19, STAI-S = 56.7 ± 10.1; p < .001). Alcohol use was predictive of reduced STAI-T scores. Predictors for increasing cortisol levels included year and use of over-the-counter medications. Predictors for short-answer results included year, laboratory role, and experience. Limitations include a limited time studied at one institution and the multifactorial, individualized nature of anxiety. Some measures of anxiety were greater in times of high COVID-19 levels, and there was evidence that anxiety was reduced for more experienced students. More work is needed to understand which factors influence student anxiety so targeted interventions can be evaluated.
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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 | 0.001 | 0.008 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".