A Novel Virtual OSCE Preparation Tool to Enhance Medical Education: Development and Usability Study (Preprint)
Bibliographic record
Abstract
BACKGROUND A significant component of Canadian medical education is learning how to approach the Objective Structured Clinical Examination (OSCE). The OSCE assesses skills imperative to good clinical practice, such as patient communication, clinical decision-making and medical knowledge. Despite the widespread implementation of this examination across all academic settings, very few preparatory resources currently exist that cater specifically to Canadian medical students. The MonkeyJacket is a novel, open-access online application built with the goal of providing medical students in Canada with an accessible and representative learning tool for the OSCE. OBJECTIVE The goal of this research study was to analyze the utility of this novel platform, with the intention of releasing an open-access version for all medical students in the near future. METHODS MonkeyJacket was developed to allow Canadian medical students the opportunity to practice their clinical examination skills with their peers using one centralized platform. The OSCE cases included in the application were developed using the Medical Council of Canada (MCC) guidelines to ensure their applicability to a Canadian setting. There are currently 75 cases covering five specialties, including cardiology, respirology, gastroenterology, neurology, and psychiatry. RESULTS The MonkeyJacket application is an online platform that allows medical students to practice clinical decision-making skills in real time with their peers by simply sharing a link. Through this application, students can practice patient interviewing, clinical reasoning, developing different diagnoses, formulating a management plan, and can receive both qualitative and quantitative feedback. Each clinical case is associated with an ‘assessment checklist’ and incorporates audio and video recording that is accessible to students after practice sessions to promote personal improvement through self-reflection. CONCLUSIONS The development of the MonkeyJacket application will transform the ways in which Canadian medical students practice for OSCEs. Currently, limited resources exist that are accessible in cost, remote in nature, and specific to MCC. By providing students with relevant clinical cases, assessment checklists, and the ability to review their own performance, MonkeyJacket fills the aforementioned gaps in medical education by introducing a unique and innovative way for medical learners to develop their patient interviewing and clinical reasoning skills. The widespread implementation of this application will promote a more competent medical workforce which will benefit the most important stakeholder in medicine - the patient.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".