Exploring the intersection of psychiatry, art, and medical education through photographic portraits
Bibliographic record
Abstract
This paper describes a technique using photographic portraits in medical education to encourage close observation, cultivate empathic curiosity, explore learners' values and beliefs, and to reveal and reflect on fundamental biases. This new and evolving educational method uses the lens of psychotherapy to explore learners' experience of the portrait in a similar way we would discuss a case in psychodynamic supervision. Through close looking and small group engagement, the facilitator creates a space for deeper reflection and collaborative exploration of the therapeutic relationship, with emphasis on countertransference and the role of prior expectations. The exercise strengthens dialectical thinking through perspective-taking, challenging implicit assumptions and fostering cultural humility. Radiologists are taught to look in every corner of the X-ray and to observe each shadow, all while evaluating the entire image. Portraits can be examined in the same way, looking for subtle clues to the personality and history of the subject. Information from other sources confirms, or sometimes profoundly changes, our evaluation. In this example, we use a historical photographic portrait to demonstrate ways of engaging medical learners as they discover common psychotherapeutic approaches. The method has the potential to enhance therapeutic encounters, improve analytical skills and reduce bias.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".