Predicting Metaethics of Nurses based on Moral Reasoning, Moral Behavior and Mental Health
Bibliographic record
Abstract
Background and Aim : Metaethics reflects knowledge of persons from moral nature, principles and processes. Metaethics is effect by many variables such as moral reasoning, moral behavior and mental health. The aimed to investigate the predicting metaethics of nurses based on moral reasoning, moral behavior and mental health. Materials and Methods : This is a cross-sectional study of correlational type. The statistical population included all nurses of Varamin hospitals in 2016 years. Totally 90 nurses were selected through simple random sampling. All of them completed the questionnaires include metaethics, moral reasoning, moral behavior and mental health. Data was analyzed using Pearson correlation and multivariate regression with stepwise model methods and with using of SPSS-19 software. Ethical Considerations : In this study, verbal informed consent of participants was obtained followed by an explanation about the purpose of the study, anonymity and confidentiality of patients' information. Findings : The results showed a positive and significant relationship between moral reasoning, moral behavior and mental health with metaethics of nurses. In a one predicted model, moral behavior, mental health and moral reasoning predicted 37/2 percent of variance of metaethics among nurses (p≤0/01). Conclusion : According to findings, moral behavior, mental health and moral reasoning were the most important predictors of metaethics of nurses. Therefore, it is suggested that planners and counselors consider the symptoms of these variables and design and Implement appropriate programs to improve the metaethics of nursing. Citation: Ashoori J. Predicting Metaethics of Nurses based on Moral Reasoning, Moral Behavior and Mental Health. Bioeth Health Law J. 2017; 1(2):44-48.
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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 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".