The Relationship Between Quality of Work Life and Job Satisfaction of Faculty Members in Zahedan University of Medical Sciences
Bibliographic record
Abstract
BACKGROUND: Quality of work life is one of the most important factors for human motivating and improving of job satisfaction. AIM: The current study was carried out aimed to determine the relationship between quality of work life and job satisfaction in faculty members of Zahedan University of Medical Sciences. METHOD: In this descriptive-analytic study, 202 faculty members of Zahedan University of Medical Sciences in 2012 were entered the study through census. The job satisfaction questionnaire of Smith and Kendall and Walton Quality of Work Life questionnaire were used for data collection. Validity and reliability of questionnaires were confirmed in previous studies. Data analysis was done using SPSS 18. The Pearson correlation coefficient and multiple regression tests were used for data analysis. RESULT: The mean score of quality of work life was 121/30±37/08 and job satisfaction was 135/98 ±33/78. There was a significant and positive correlation between job satisfaction of faculty members and their quality of work life (P=0.003). In addition, two components of quality of work life "adequate and fair compensation" (β=0.3) and "Social Integration" (β=0.4) can predict job satisfaction of faculty members. CONCLUSION: According to correlation between job satisfaction and quality of work life in faculty members, job satisfaction can be improved through the changing and manipulating the components of quality of work life and in this way; the suitable environment for organization development should be provided.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".