Ethical Considerations in Cross-Linguistic Nursing
Bibliographic record
Abstract
This article reviews empirical evidence and ethical norms in cross-linguistic nursing. Empirical evidence highlights that linguistic barriers between nurses and patients can perpetuate discrimination and compromise nursing care. There are significant organizational and relational challenges involved in ensuring adequate use of interpreters by nurses. Some evidence suggests that linguistic barriers are particularly problematic for nurses when compared with physicians. A comparative analysis of nursing ethical norms for cross-linguistic nursing was conducted using the codes of ethics of the American Nurses Association, the Canadian Nurses Association, and the International Council of Nurses. Five principal ethical norms for cross-linguistic nursing were identified: (1) respect for the patient as a unique person; (2) respect for the patient's right to self-determination; (3) respect for patient privacy and confidentiality; (4) responsibility for one's own competence, judgment, and action; and (5) responsibility to promote action better to meet the needs of patients, families, and groups.
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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 | 0.156 | 0.248 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.060 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.013 | 0.015 |
| 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".