Guidance for Ethical Leadership in Nursing Codes of Ethics: An Integrative Review
Bibliographic record
Abstract
There has been limited attention to ethical leadership for formal nurse leaders around the world. Assuming that codes of ethics provide meaningful standards of what is expected of health professionals, what specific guidance for ethical leadership is available to formal nurse leaders in national nursing codes of ethics? We conducted an integrative review of national nursing codes of ethics for 131 member countries of the International Council of Nurses (ICN). In the ICN Code, nurse managers/leaders are highlighted for their role in ethical practice. With the exception of the US, no other country code focuses as much attention on formal nurse leaders. While all country codes (except the United States) implicitly group nurses, practitioners and managers together, most codes do not provide meaningful guidance for formal nurse leaders. The level of ethical guidance provided to formal nurse leaders in national nursing codes of ethics is lacking. However, creating a separate code of ethics for formal nurse leaders is not the answer. Rather, including specific guidance in nursing codes of ethics not only informs nurses about what they can expect of nurse leaders but also allows formal nurse leaders to use the code with their own senior leaders, conveying what their professional body expects of them.
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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 | 0.024 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| 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".