The influence of ethics education on awareness of nursing students with no clinical experience regarding the code of ethics: A case study
Bibliographic record
Abstract
The purpose of this study was to evaluate the influence of ethics education on Korean nursing students’ awareness regarding the code of ethics. This study employed a one group pretest-posttest design. The participants were 48 sophomore nursing students with no clinical experience from a city in Korea. In this study, ethics education involved lectures related to “nursing ethics and professionalism”. Data were collected in September and December 2016. The collected data were analyzed using means, standard deviations, and paired t tests. The mean score on awareness regarding the code of ethics increased from 3.75 to 4.40 after the ethics education (t = 6.350, p < .001). Scores on the sub-domains of nurses and patients, obligations of nurses as experts, and nurses and cooperators also increased significantly (t = 6.224, t = 5.436, t = 4.969, respectively, all p < .001). In this study, ethics education was found to raise nursing students’ awareness regarding the code of ethics. Both awareness and practice of the code of ethics are crucial for adherence to the moral standards of professional behaviors required from medical professionals. Ethics education needs to be implemented, as it is indispensable for building a desirable ethical value system among future medical professionals and for enabling them to put ethical behaviors into practice by improving their moral judgment and sensitivity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".