Discovering clinical phronesis
Bibliographic record
Abstract
Phronesis is often described as a 'practical wisdom' adapted to the matters of everyday human life. Phronesis enables one to judge what is at stake in a situation and what means are required to bring about a good outcome. In medicine, phronesis tends to be called upon to deal with ethical issues and to offer a critique of clinical practice as a straightforward instrumental application of scientific knowledge. There is, however, a paucity of empirical studies of phronesis, including in medicine. Using a hermeneutic and phenomenological approach, this inquiry explores how phronesis is manifest in the stories of clinical practice of eleven exemplary physicians. The findings highlight five overarching themes: ethos (or character) of the physician, clinical habitus revealed in physician know-how, encountering the patient with attentiveness, modes of reasoning amidst complexity, and embodied perceptions (such as intuitions or gut feeling). The findings open a discussion about the contingent nature of clinical situations, a hermeneutic mode of clinical thinking, tacit dimensions of being and doing in clinical practice, the centrality of caring relations with patients, and the elusive quality of some aspects of practice. This study deepens understandings of the nature of phronesis within clinical settings and proposes 'Clinical phronesis' as a descriptor for its appearance and role in the daily practice of (exemplary) physicians.
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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 | 0.018 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.031 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".