A Study of the Relationship Between Nurses’ Professional Self-Concept and Professional Ethics in Hospitals Affiliated to Jahrom University of Medical Sciences, Iran
Bibliographic record
Abstract
BACKGROUND: Commitment to ethics usually results in nurses' better professional performance and advancement. Professional self-concept of nurses refers to their information and beliefs about their roles, values, and behaviors. The objective of this study is to analyze the relationship between nurses' professional self-concept and professional ethics in hospitals affiliated to Jahrom University of Medical Sciences. METHODS: This cross sectional-analytical study was conducted in 2014. The 270 participants were practicing nurses and head-nurses at the teaching hospitals of Peimanieh and Motahari in Jahrom University of Medical Science. Sampling was based on sencus method. Data was collected using Cowin's Nurses' self-concept questionnaire (NSCQ) and the researcher-made questionnaire of professional ethics. RESULTS: The average of the sample's professional self-concept score was 6.48±0.03 out of 8. The average of the sample's commitment to professional ethics score was 4.08±0.08 out of 5. Based on Pearson's correlation test, there is a significant relationship between professional ethics and professional self-concept (P=0.01, r=0.16). CONCLUSION: In view of the correlation between professional self-concept and professional ethics, it is recommended that nurses' self-concept, which can boost their commitment to ethics, be given more consideration.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".