(Re)defining nursing leadership: On the importance of <i>parrhèsia</i> and subversion
Bibliographic record
Abstract
AIM: Through a review of philosophical and theoretical constructs, this paper offers insight and guidance as to ways in which nurse leaders may operationalize advocacy and an adherence to nursing's core ethical values. BACKGROUND: The US health care system works in opposition to core nursing values. Nurse leaders are obliged to advocate for the preservation of ethical care delivery. EVALUATION: This paper draws upon the philosophies of Fromm, Foucault, and Deleuze and Guattari to critically review the functions of nurse leaders within a capitalist paradigm. KEY ISSUE: Key emergent issues in the paper include health care and capitalism and the nurse leader's obligations towards advocacy. CONCLUSION: The nurse leader acts as parrhèsia in viewing truth telling as a duty critical to improving the lives of patients. Ramifications of the decisions by those in power have even greater impact in institutions that serve those with little to no political agency. IMPLICATIONS FOR NURSING MANAGEMENT: The nurse leader has a freedom and platform that their patients do not and must take the courageous risk of choosing to speak. This paper serves as a call to action for nurse leaders to urgently address the current state of US health outcomes.
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.008 |
| 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".