Showing Your Thinking: Using Mind Maps to Understand the Gaps Between Experienced Emergency Physicians and Their Students
Bibliographic record
Abstract
BACKGROUND: Clinical teaching faculty rely on schemas for diagnosis. When they attempt to teach medical students, there may be a gap in the interpretation because the students do not have the same schemas. The aim of this analysis was to explore expert thinking processes through mind maps, to help determine the gaps between an expert's mind map of their diagnostic thinking and how students interpret this teaching artifact. METHODS: A novel mind-mapping approach was used to examine how emergency physicians (EPs) explain their clinical reasoning schemas. Nine EPs were shown two different videos of a student interviewing a patient with possible venous thromboembolism. EPs were then asked to explain their diagnostic approach using a mind map, as if they were thinking to a student. Later, another medical student interviewed the EPs to clarify the mind map and revise as needed. A coding framework was generated to determine the discrepancy between the EP-generated mind map and the novice's interpretation. RESULTS: Every mind map (18 mind maps from nine individuals) contained some discrepancy between the expert's mind and novice's interpretation. From the qualitative analysis of the changes between the originally created mind map and the later revision, the authors developed a conceptual framework describing types of amendments that students might expect teachers to make in their mind maps: 1) substantive amendments, such as incomplete mapping; and 2) clarifications, such as the need to explain background for a mind map element. CONCLUSION: Emergency physician teachers tend to make jumps in reasoning, most commonly including incomplete mapping and maps requiring clarifications. Educating EPs on these processes will allow modification of their teaching modalities to better suit learners.
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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 | 0.017 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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 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".