Pre-Clerkship Medical Students’ Perspectives on the Learning Environment at Arabian Gulf University, Bahrain-Insights on Learning Experiences at Arabian Gulf University
Bibliographic record
Abstract
BACKGROUND: The educational environment in medical schools is a critical factor influencing students' academic performance and overall learning experience. This study aimed to explore pre-clerkship medical students’ perceptions of the learning environment at Arabian Gulf University (AGU) in the Kingdom of Bahrain. AIM: This study sought to evaluate the educational environment for pre-clerkship medical students at AGU using the Dundee Ready Education Environment Measure (DREEM), assessing both strengths and areas requiring improvement. METHODS: A bilingual (Arabic and English) version of the DREEM instrument, validated for diagnosing the quality of educational environments, was administered to 324 undergraduate pre-clerkship students at AGU across the second, third, and fourth academic years. Data analysis employed both parametric and non-parametric tests to assess the relationship between DREEM scores and variables such as academic year, gender, nationality, and academic performance. RESULTS: There were no significant differences in students’ perceptions based on academic year or gender. However, significant differences were observed in nationality and academic achievement (p = 0.048 and p = 0.018, respectively). CONCLUSION: The findings indicate that pre-clerkship students generally perceive AGU’s learning environment positively. However, the study identified specific areas within the educational environment that may benefit from targeted improvement strategies to enhance the overall learning experience.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".