Exploring Students’ Perceptions of the Educational Environment in a Caribbean Veterinary School: A Cross-Sectional Study
Bibliographic record
Abstract
Educational environment has a significant impact on students’ learning and academic achievement. The aim of this article was to explore the perception of veterinary school students’ regarding their educational environment at the University of the West Indies. In this cross-sectional study, the Dundee Ready Education Environment Measure (DREEM) was administered to veterinary undergraduate students from year 2 to year 5. The DREEM questionnaire consists of 50 items with five subscales: students’ perceptions of learning, students’ perceptions of teachers, students’ academic self-perceptions, students’ perceptions of atmosphere, and students’ social self-perceptions. Each item was scored on a 5-point Likert scale ranging from strongly disagree (0) to strongly agree (4). The Cronbach’s alpha for the overall DREEM score was 0.92, and for the five subscales, it ranged from 0.66 to 0.83. A total of 99 students responded (response rate: 86%). The students’ overall DREEM mean score was 106.59 out of the global mean score of 200, indicating that students’ perception of the educational environment was generally more positive than negative. In the five DREEM subscales, students were found to have a more positive perception of learning (55.15%); students’ perception of teachers was generally positive (61.41%); and their perception of academic atmosphere was also positive (57.75%). Conversely, students’ academic self-perception (51.41%) and social self-perception (42.61%) trended negatively. The findings suggest that improvement is needed in significant areas in the veterinary school, including curriculum review, faculty development, provision of sports and cultural facilities, stress management, and academic support systems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".