Kazan State Medical University Survey after the Use of CyberPatient<sup>TM</sup> during COVID-19
Bibliographic record
Abstract
The COVID-19 pandemic created challenges for medical education, particularly for the acquisition of clinical skills. At Kazan State Medical University (KSMU), we used an online simulation platform called CyberPatientTM (CP) to provide a clinical environment in a virtual space with a variety of patients for students to practice their clinical skills. In this study, we surveyed 59 students who used CP in the 2020 spring semester. This survey’s objectives were to gather the students’ opinion on usability, value, efficacy and impact of the CP platform. Survey results revealed that CP is used significantly (P 0.0001) more when it is an integral part of the curriculum, it was not difficult to operate the system (96.6%); the students were satisfied with the number, quality and variety of the cases in CP platform (93.3%); over 90% of students identified CP valuable; a significant number of students (p 0.001) believed that CP was effective and 89.9% of students believed that CP had a measurably high impact on their knowledge and experience. This study concludes that the use of virtual clinical environments such as CP is perceived by students to be valuable and effective in learning clinical skills particularly during this pandemic and in the post-pandemic period when the access of students to clinical environments remains limited.
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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 | 0.003 |
| 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) | 0.004 | 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".