Effectiveness of ethical issues teaching program on knowledge, ethical behavior and ethical stress among nurses
Bibliographic record
Abstract
Background : New technology, economic constraints, scarce resources and conflicting values have set expectations for nurses’ ethical competence. Everyday ethical issues in nursing practice attract little attention but can create stress for nurses. Nurses often feel uncomfortable in addressing the ethical issues they encounter in patient care. The aim of the study is to examine the effect of teaching program regarding ethical issues on nurses knowledge, ethical behavior, frequency and degree of ethical stress. Subjects and methods : Research design is A Quasi-experimental design was used in the study. The study was conducted among staff nurses working at Mansoura Emergency Hospital which provides a wide spectrum of health services at Delta Region. Three tools were used for data collection namely: Ethical Issues Knowledge Questionnaire, Observational checklist for Ethical Behavior and Ethical Issues Stress scale. Results: The study findings concludes that there was a strong correlation between Knowledge of Nursing ethics and Ethical behavior after the teaching program. In addition strong correlation found between the Knowledge and frequency of ethical stress in post test. Conclusion & Recommendations: Nurses face daily ethical challenges in the provision of quality health care. Ethical issues teaching program has positive influence in improving their knowledge, reduction in the frequency and degree of stress. It is recommended that ethics training cannot take place just once in a training room, but needs on-going support at all levels of the organization. The training should include what ethics is, along with actual examples of relevant situations, and how to explore an ethical dilemma through interacting with others.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".