Ethical competence: An integrative review
Bibliographic record
Abstract
BACKGROUND: Ethics, being a fundamental component of nursing practice, must be integrated in the nursing education curriculum. Even though different bodies are promoting ethics and nursing researchers have already carried out work as regards this concept, it still remains difficult to clearly identify the components of this competence. OBJECTIVE: This integrative review intends to clarify this point in addition to better defining ethical competence in the context of nursing practice. METHOD: An integrative review was carried out, for the 2009-2014 period, in the CINAHL, MEDLINE, and EMBASE databases and in the journal Nursing Ethics. The keywords nursing ethics or ethical competence were used in order to make sure to widely encompass the concept of "ethical competence" in the case of a university curriculum in nursing. In the end, 89 articles were selected. Ethical consideration: We have respected the ethical requirements required regarding the sources and authorship. There is no conflict of interest in this literature review. RESULTS: Ethical sensitivity, Ethical knowledge, Ethical reflection, Ethical decision-making, Ethical action, and Ethical behavior are the most frequently used terms with regard to ethical competence in nursing. They were then defined so as to better ascertain the possible components of ethical competence in nursing. CONCLUSION: Even though ethical competence represents a sine qua non competence in nursing practice, no consensus can be found in literature with respect to its definition. The identification of its components and their relationships resulting from this integrative review adds to the clarification of its definition. It paves the way for other studies that will contribute to a better understanding of its development, especially among nursing students and practicing nurses, as well as the factors that may exert an influence. More adapted education strategies can thus be put forward to support its development.
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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 | 0.012 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.020 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".