Relational ethics and advocacy in nursing: literature review
Bibliographic record
Abstract
AIM: The purpose of this paper is to analyse themes from accounts of nurses' experiences with advocacy that may expand our understanding of advocacy in nursing practice. BACKGROUND: Although the ethical obligation to advocate is universal, a lack of clarity persists about the nature of advocacy in nursing practice. METHOD: This discussion of advocacy is based on a synthesis of qualitative studies that focus on nurses' experiences with advocacy in practice. Empirical studies were retrieved through searches on the CINAHL and Academic Search Premier databases for the years 1993-2005. The search terms used were advocacy, advocate role, ethics, nursing practice and qualitative research. FINDINGS: Empirical studies related to the role of advocacy in nursing are limited in number. Nurses' experiences with advocacy reveal important themes in relation to factors that influence the application of advocacy in nursing practice. Evidence suggests that the nature and context of relationships plays a significant role in influencing the enactment of advocacy. CONCLUSION: The application of advocacy in nursing practice is complex. The philosophy of relational ethics emphasizes the contextual features of relationships. An examination of relational ethics as it applies to advocacy in nursing brings us closer to a deeper understanding of the process by which nurses make advocacy choices in practice, and raises implications for the development of advocacy in nurses' practice. Advocacy is universally considered a moral obligation in nursing practice, and thus advancement of our knowledge about its nature in nursing is relevant to nursing across multiple contexts and cultures.
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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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.013 | 0.020 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".