The digital age: is this the future of medical education? A cross-sectional study to assess medical students’ opinions about e-learning in psychiatry undergraduate medical education
Bibliographic record
Abstract
OBJECTIVES: The main objectives were to assess medical students' opinions about e-learning in psychiatry undergraduate medical education, and to investigate a possible relationship between learning styles and preferences for learning modalities. METHOD: During the academic year 2009/2010, all 231 senior Royal College of Surgeons in Ireland (RCSI) medical students in their penultimate year of study were invited to answer a questionnaire that was posted online on Moodle, the RCSI virtual learning environment. RESULTS: In all, 186 students responded to the questionnaire, a response rate of 80%. Significantly more students stated a preference for live psychiatry tutorials over e-learning lectures. Students considered flexible learning, having the option of viewing material again and the ability to learn at one's own pace with e-learning lectures, to be more valuable than having faster and easier information retrieval. CONCLUSION: Students prefer traditional in-class studying, even when they are offered a rich e-learning environment. Understanding students' learning styles has been identified as an important element for e-learning development, delivery and instruction, which can lead to improved student performance.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".