E-Learning Impact on Veterinary Medical Students’ Mental Health during the COVID-19 Pandemic
Bibliographic record
Abstract
Veterinary medical students are known to have significant levels of mental illness. The COVID-19 pandemic and the shift to online learning have brought increased psychological stress. We used a web-based survey to ask 415 veterinary medical students from Portugal about the impact of the pandemic and online learning on their anxiety levels. Results were analyzed using logistic regressions and Spearman's correlation. Results indicated that 15.4% had no symptoms of anxiety, 39.5% experienced mild anxiety, 21.4% had moderate anxiety, and 23.6% experienced severe anxiety. Having difficulty sleeping, the stress associated with confinement, and family conflicts were risk factors for anxiety, while being male was found to be protective. Most veterinary medical students (77%) were satisfied with online learning. The university's adaptation to online teaching and time spent participating in online classes were significantly associated with anxiety. Due to the known fragility of veterinary medical students' mental health, this group should be monitored and supported closely during life-disrupting events such as public health emergencies.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".