Evaluation of a simulation-based ophthalmology education workshop for medical students: a pilot project
Bibliographic record
Abstract
BACKGROUND/AIMS: Ophthalmology is an under-represented specialty in many medical school curriculums resulting in reduced confidence in medical students and clinicians when dealing with eye conditions. Our study evaluates the impact of a simulation-based education (SBE) workshop to train medical students in ophthalmology. METHODS: Second-year medical students were invited to participate in a two-day (eight-hour) simulation-based ophthalmology workshop. Standardised patients, free-to-use simulators, and low-cost eye models were used to teach eye anatomy, physiology, pathologies, skills (slit-lamp, ophthalmoscopy etc.), and eye procedures (cataract surgery, eye lasers etc.). Learners filled questionnaires to evaluate their ophthalmology interest, confidence, and knowledge before the workshop, immediately after the workshop, and three months later. They also answered a feedback survey on the workshop's quality and usefulness immediately after the workshop. RESULTS: Nine students, including six females and three males, participated in the workshop. Pre-workshop, learners' mean self-reported confidence in dealing with ophthalmology patients was 1.8/5 and mean self-reported interest in pursuing an ophthalmology residency was 2.6/5 on a Likert-scale-based questionnaire (on a scale of 1-5). Learners scored a mean of 8.4/15 on an ophthalmology knowledge questionnaire with fifteen questions. Post-workshop (immediate), their mean self-reported confidence was 3.4/5 (p = 0.0001), interest in pursuing an ophthalmology residency was 3.2/5 (p = 0.022), and score on the ophthalmology questionnaire was 13/15 (p = 0.0001). Three months later, students' self-reported mean confidence was 3.2/5 (p = 0.0001), the likelihood of choosing ophthalmology residency was 2.8/5 (p = 0.59), and score on the ophthalmology knowledge questionnaire was 11/15 (p = 0.006). The feedback survey showed that all students found the workshop relevant, comprehensive, easy to understand, and that they gained knowledge/skills applicable to their future clinical practice. CONCLUSIONS: A small group SBE ophthalmology workshop improves learners' knowledge, skills, and confidence using an approach they find interesting, with low cost and time investment. TRIAL REGISTRATION: Not applicable.
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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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".