Using<i>phronesis</i>instead of ‘research‐based practice’ as the guiding light for nursing practice
Bibliographic record
Abstract
Abstract Phronesis , a popular Aristotelian concept that emphasizes deliberation and moral action, should replace the phrase ‘research‐based practice’ as the guiding light for nursing practice. Knowledge from research is still essential, of course, but is insufficient by itself for practice. In this paper, the author describes assumptions behind the apparent superiority of research‐based knowledge, and offers a critique of this position. One critique is that by automatically accepting the superiority of research‐based knowledge other types of knowledge (e.g. intuitive, ethical, personal) are devalued. A second critique is that undeliberated, indiscriminate use of research findings may lead to inappropriate nursing practice. Phronesis is then described, and its application to nursing. For example, phronesis requires that the context of the situation be considered very carefully before acting. Aristotle stated that the goal of personal phronesis is to reach eudaimonia , or genuine happiness or ‘human flourishing’. Infusing nursing practice with phronesis means that an anthropomorphized discipline's eudaimonia would be the eudaimonia of patients. That is, nursing practice would be guided by a desire for patients' genuine happiness or human flourishing. The final section of the paper offers rebuttals to potential criticisms.
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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 | 0.053 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.093 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.008 | 0.015 |
| 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".