A Research to Determine the Ethical Climate Perception of Nurses Who Work In Private Hospitals
Bibliographic record
Abstract
Objective: It’s imperative that organizations that want to exist in the business world where competition is intense and that want to ensure should create a positive ethical climate perception. The aim of this study was to determine the level of perceptions of the ethical climate of nurses who work in a private hospital and to determine whether ethical climate perceptions differ according to demographic characteristics. Methods: The study was conducted between July-September 2018 on 154 nurses working at two private hospitals(in Istanbul and Yalova). Research data were collected from nurses working in these hospitals. This is a descriptive cross-sectional study. Results: According to findings, it was determined that the ethical climate perception of the participants was generally positive. In dimensions, ethical climate perception was found most positive in patient dimension, while most negative in physicians dimension. At findings, the perception of ethical climate according to the gender of the participants differed statistically in the managers dimension and perception of ethical climate according to working time in hospital differed in patients dimension(p<0.05). Perception of ethical climate didn’t differed significantly with age, educational and marital status of participants (p>0.05). Conclusions: It was determined that the hospital ethical climate perceptions of the nurses in the study were highly positive, and this perception was influenced by gender and the year of work. The sub-dimension, where the ethical climate perception was the most negative, was the physician dimension. It’s thought that the results obtained can be a guide in supporting positive ethical behavior.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".