Job Satisfication and External Effective Factors in Operating Room Nurses Working Educational Hospitals in 2015: A Cross-Sectional Questionnaire Study
Bibliographic record
Abstract
INTRODUCTION & AIM: Identification of effective factors on job satisfaction can impact the efficiency and quality of health services and personal life. The Aim of this study was to investigate, Job Satisfaction and External Effective Factors in Operating Room Nurses Educational Hospitals of Zahedan in 2015.MATERIALS & METHODS: This descriptive–analytical study was conducted on 137 operating room Nurses of Ali-Ebne-Abitaleb, Khatam-Al-Anbia and Alzahra hospital in Zahedan, Iran, enrolled through the convenience sampling in 2015. The data were collected by the valid and reliable job satisfaction standard questionnaire or Herzberg's Dual-Factor theory. To analyze the information, descriptive statistics, Chi-square & Pearson correlation test were applied by use of SPSS v. 21 software.FINDINGS: The results found that the most effective factors of job satisfaction were job safety with an average 4/78, work qualification with an average 4/32 and suitable salary with an average 3/91 respectively. Only 45.98% of the nurses experienced moderate job satisfaction, and only 25.54% of them had high job satisfaction. Moreover, job satisfaction was significantly related to the type of employment (p=0. 019), work background (p=0. 029) and turn of work (p=0. 034).CONCLUSION: The data of this study found that, notice and planning in preparing job safety, work qualification and proper salary may play a more effective role in improving employees’ performance than any other factors.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".