Perspectives on phronesis in professional nursing practice
Bibliographic record
Abstract
The concept of phronesis is venerable and is experiencing a resurgence in contemporary discourses on professional life. Aristotle's notion of phronesis involves reasoning and action based on ethical ideals oriented towards the human good. For Aristotle, humans possess the desire to do what is best for human flourishing, and to do so according to the application of virtues. Within health care, the pervasiveness of economic agendas, technological approaches and managerialism create conditions in which human relationships and moral reasoning are becoming increasingly de-valued. This creates a tension for nurses, and nursing leaders, as the desire to do what is morally right is often in conflict with contextual demands. In this paper, Aristotle's writing on phronesis is examined with a focus on his classic conceptions of eudaimonia, the virtues, deliberation, judgement, and praxis. Building on Aristotle's work, a number of contemporary views are explored with a focus on what various conceptualizations offer for the discipline of nursing. These expanded conceptions of phronesis include attention to: embodiment in practice; open-mindedness including the capacity to stay curious and open to recognizing what we do not know; perceptiveness as a disposition towards insight and aesthetic understanding; and reflexivity as an ongoing process of interrogation and inquiry into ourselves and our actions. Drawing on these concepts, we discuss the affordances of phronesis as a morally informed guiding force to attend to modern-day challenges in nursing practice and nursing leadership.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.062 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".