Educational challenges in teaching nursing ethics: Perspectives of educators in Japan
Bibliographic record
Abstract
Background: While nursing ethics is becoming accepted as an independent subject in Japanese universities, there are many issues concerning the education. The purpose of this study was to anew investigate nursing ethics programs in university, to reveal the difficulties that nursing ethics educators in Japanese nursing bachelor’s degree programs faced and educational challenges, and to examine the best form of nursing ethics education.Methods: A self-administered questionnaire survey to nursing ethics educators in 235 nursing bachelor’s degree programs in Japan was conducted. The questionnaire mainly asked about an overview of nursing ethics programs, the difficulties educators experienced and educational challenges, and the association of ethics education with nursing practice.Results: The return rate of the questionnaire was 29.7%. Methods of group discussion based on case studies were common, and patients’ rights and analytical approaches to ethical issues were common topics. Many of the subjects faced difficulties in nursing ethics education: curriculum, students’ circumstances, students’ readiness and abilities, the characteristics of ethical problems, and educators’ abilities. The educators suggested that students should continue to learn nursing ethics over a few school years according to a curriculum built in a step-by-step, systematic manner.Conclusions: This survey indicated that discussions regarding what educational goals in what school year should be offered to students were needed at each university. Cooperation between educators and practical training instructors on the contents of education, and a clear definition of expertise necessary for teaching nursing ethics were challenges to be accomplished as well.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".