Web-Based Immersive Patient Simulator as a Curricular Tool for Objective Structured Clinical Examination Preparation in Surgery: Development and Evaluation
Bibliographic record
Abstract
BACKGROUND: Objective Structured Clinical Examination is a standard method of testing declarative and process knowledge in clinical core competencies. It is desirable that students undergo Objective Structured Clinical Examination training before participating in the exam. However, establishing Objective Structured Clinical Examination training is resource intensive and therefore there is often limited practice time. Web-based immersive patient simulators such as ALICE (Artificial Learning Interface of Clinical Education) can possibly fill this gap as they allow for the training of complex medical procedures at the user's individual pace and with an adaptable number of repetitions at home. ALICE has previously been shown to positively influence knowledge gain and motivation. OBJECTIVE: Therefore, the aim of this study was to develop a Web-based curriculum that teaches declarative and process knowledge and prepares students for a real Objective Structured Clinical Examination station. Furthermore, we wanted to test the influence of ALICE on knowledge gain and student motivation. METHODS: A specific curriculum was developed in order to implement the relevant medical content of 2 surgical Objective Structured Clinical Examination stations into the ALICE simulator framework. A total of 160 medical students were included in the study, where 100 students had access to ALICE and their performance was compared to 60 students in a control group. The simulator performance was validated on different levels and students' knowledge gain and motivation were tested at different points during the study. RESULTS: The curriculum was developed according to the Kern cycle. Four virtual clinical cases were implemented with different teaching methods (structured feedback, keynote speech, group discussion, and debriefing by a real instructor) in order to consolidate declarative and process knowledge. Working with ALICE had significant impact on declarative knowledge gain and Objective Structured Clinical Examination performance. Simulator validation was positive for face, content, construct, and predictive validity. Students showed high levels of motivation and enjoyed working with ALICE. CONCLUSIONS: ALICE offers Web-based training for Objective Structured Clinical Examination preparation and can be used as a selective didactic intervention as it has positive effect on knowledge gain and student motivation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".