Collaborative Development of Feedback Concept Maps for Virtual Patient–Based Clinical Reasoning Education: Mixed Methods Study
Bibliographic record
Abstract
Background: Concept maps are a suitable method for teaching clinical reasoning (CR). For example, in a concept map, findings, tests, differential diagnoses, and treatment options can be documented and connected to each other. When combined with virtual patients, automated feedback can be provided to the students' concept maps. However, as CR is a nonlinear process, feedback concept maps that are created together by several individuals might address this issue and cover perspectives from different health professionals. Objective: In this study, we aimed to develop a collaborative process for creating feedback concept maps in virtual patient-based CR education. Methods: Health professionals of different specialties, nationalities, and levels of experience in education individually created concept maps and afterward reached a consensus on them in structured workshops. Then, medical students discussed the health professionals' concept maps in focus groups. We performed a qualitative content analysis of the transcribed audio records and field notes and a descriptive comparison of the produced concept maps. Results: A total of 14 health professionals participated in 4 workshops, each with 3-4 participants. In each workshop, they reached a consensus on 1 concept map, after discussing content and presentation, as well as rationales, and next steps. Overall, the structure of the workshops was well-received. The comparison of the produced concept maps showed that they varied widely in their scope and content. Consensus concept maps tended to contain more nodes and connections than individual ones. A total of 9 medical students participated in 2 focus groups of 4 and 5 participants. Their opinions on the concept maps' features varied widely, balancing between the wish for an in-depth explanation and the flexibility of CR. Conclusions: Although the number of participating health professionals and students was relatively low, we were able to show that consensus workshops are a constructive method to create feedback concept maps that include different perspectives of health professionals with content that is useful to and accepted by students. Further research is needed to determine which features of feedback concept maps are most likely to improve learner outcomes and how to facilitate their construction in collaborative consensus workshops.
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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 | 0.128 |
| 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 | 0.000 | 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".