The power of metaphor in medical education: fostering shared understanding in complex conversations
Bibliographic record
Abstract
Effective communication in medical education, particularly around complex or emotionally charged topics, remains a significant challenge. While numerous feedback frameworks exist, less attention has been paid to optimizing fundamental linguistic tools like metaphor. Contemporary theory posits metaphor not merely as linguistic ornamentation, but as a conceptual tool that can deepen and even create understanding. This article employs a conceptual review format, drawing on established principles from cognitive psychology, narrative medicine, and adult learning theory. It utilizes illustrative vignettes to present practical applications of metaphor in clinical training environments. We identify, and explore, three key areas where metaphor can be a powerful tool for medical educators: (i) Teaching clinical reasoning, (ii) Opening difficult conversations, (iii) Facilitating discussions on professional identity formation. We identify challenges with metaphor use, such as male gender coded metaphor, and explore areas of caution for the use of metaphor in medical education, including intercultural communication and communication with neurodiverse individuals. We use a strategy called SAFE (Slow down, Acknowledge and Apologize, Follow-up, Explain), as a simple mnemonic for educators to recognize and respond when a metaphor misses the mark. Metaphor is experiencing a renaissance in medical education as a vital tool for fostering shared understanding. Its intentional use can enhance teaching in complex domains like clinical reasoning, difficult conversations, and professional identity. Educators are encouraged to adopt this tool mindfully, with awareness of its potential pitfalls, and a readiness to employ the SAFE strategy when a miss is perceived.
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 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 | 0.022 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.026 |
| Scholarly communication | 0.010 | 0.019 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.004 | 0.005 |
| 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".