‘He’s not lonely, he’s nearly been killed’ Teaching Disclosure of Medical Error through the Virtual Patient – an Exploratory Study
Bibliographic record
Abstract
<b>Background:</b> The use of simulation and technology enhanced learning in medical education has increased in the last ten years and their role is well established. Further research is however required to determine the usefulness of combined technology enhanced learning/simulation tools such as the virtual patient, particularly in the area of communication skills. Disclosure of medical error was identified as an important topic in medical communication where formal training is lacking. The aim of this research therefore was to explore how a virtual patient intervention supports student learning of disclosure of medical error. <b><br></b><b>Methods: </b>The virtual patient was developed using an evidence-based approach. A qualitative methodology was used to explore the study aims and objectives. Fourteen RCSI SC1 students participated in the data collection component of this study, which involved direct participant observation (with screenshare and think aloud) while completing the virtual patient and a semi-structured interview. <b><br></b><b>Results:</b> All participants engaged successfully with the virtual patient, a small number of participants selected suboptimal responses at varying steps. Think aloud data captured extensive analysis and reasoning applied by participants in completing the virtual patient. The virtual patient provided a positive learning experience for participants with favourable features of the virtual patient highlighted, including reality of the simulation and opportunity for repetitive practice. The virtual patient was found to support learning of disclosure of medical error in terms of content and feedback through rigorous script development, use of the Calgary Cambridge framework and inclusion of timely feedback. <br><b>Conclusion:</b> The results of this study underline the usefulness of the virtual patient in supporting student learning in disclosure of medical error. This research contributes to the existing body of virtual patient research identifying effective educational features of the virtual patient and adds another layer supporting the use of an evidence-based virtual patient in teaching communication skills in the challenging area of disclosure of medical error.<br>
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".