Searching for ethical leadership in nursing
Bibliographic record
Abstract
BACKGROUND: Attention to ethical leadership in nursing has diminished over the past several decades. OBJECTIVES: The aim of our study was to investigate how frontline nurses and formal nurse leaders envision ethical nursing leadership. RESEARCH DESIGN: Meta-ethnography was used to guide our analysis and synthesis of four studies that explored the notion of ethical nursing leadership. PARTICIPANTS AND RESEARCH CONTEXT: These four original studies were conducted from 1999-2008 in Canada with 601 participants. ETHICAL CONSIDERATIONS: Ethical approval from the original studies covered future analysis. FINDINGS: Using the analytic strategy of lines-of-argument, we found that 1) ethical nursing leadership must be responsive to practitioners and to the contextual system in which they and formal nurse leaders work, and 2) ethical nursing leadership requires receiving and providing support to increase the capacity to practice and discuss ethics in the day-to-day. DISCUSSION AND CONCLUSION: Formal nurse leaders play a critical, yet often neglected role, in providing ethical leadership and supporting ethical nursing practice at the point of patient care.
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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 | 0.060 | 0.134 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".