The relationship between physicians and nurses in hospitals affiliated with Zanjan University of Medical Sciences, Iran
Bibliographic record
Abstract
Introduction: The establishment of a healthy relationship between healthcare professionals is required for resolving healthcare issues. The professional relationship between physicians and nurses are quite decisive and vital factor for patient care. Therefore, the purpose of this study was to examine the quality of relationship between physicians and nurses in hospitals affiliated with Zanjan University of Medical Sciences, Iran.Methods: In this descriptive-analytic study, a cross-sectional method was used. Nurses working in hospitals affiliated with Zanjan University of Medical Sciences, Iran were selected randomly. A demographic data and a 32-item questionnaire related to the professional relationship between physicians and nurses were used for data collection. Of 160 nursing staff 96 nurses returned the questionnaire. Descriptive and analytical statistics were used for data analysis via the SPSS software.Results: About 75.8% of the nurses were female and 76.8% held a bachelor's degree. Also, 86.4% of them had rotational shift works. Many of them (66.2%) held below 15 years of work experience and 59.5% received no reward to make a relationship with physicians. Moreover, 76.8% of the samples mentioned that non-existence of fixed work shifts and extra shifts created stressful and tedious conditions leading to ineffective nurse-physician relationships.Conclusions: There were major shortcomings within nurse-physician relationships. Hence, the promotion of physicians’ knowledge on nurses’ career and nurses’ welfare is required for advancing professional relationships between nurses and physicians.
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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 | 0.001 | 0.005 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".