The Powers of a Fish: Clinical Thinking, Humanistic Thinking, and Different Ways of Knowing
Bibliographic record
Abstract
How are ways of knowing similar between clinical reasoning and the humanities, and can the latter be used to elucidate the former? This commentary considers a conceptual model proposed by Prince and colleagues in this issue to explore the different ways of knowing in art and medicine. Their proposed model links 2 approaches to clinical reasoning with an analytic approach said to be characteristic of the humanities-visual thinking strategies (VTS)-to teach skills in clinical reasoning. They suggest that the VTS approach aligns well with the 2 clinical reasoning approaches and use this relationship to argue for the introduction of the humanities into graduate medical education. However, is VTS truly an exemplar of analytic approaches used in the humanities? The approach to clinical decision making is a version of what Donald A. Schön calls technical rationality, but what is the epistemology used in the humanities and art? This commentary explores this question through the perspective of hermeneutics, a branch of philosophy that centers on an interpretive understanding of art, and through art, a way of knowing the self, others, and the world. In contrast to limiting the focus of the humanities in medical education to sharpening the powers of observation and analytical thinking, the author argues that art also offers a way to explore the challenges and triumphs of providing care to those in need and to explore the meanings, feelings, and experiences of living and dying. It offers a way of understanding and expressing the moral dilemmas of our time that aspires toward the aesthetic, philosophical, and existential truths of a life in medicine.
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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 | 0.027 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.168 |
| Scholarly communication | 0.017 | 0.038 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.002 | 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".