A Study of the Relationship between the Quality of Working Life and Mental Health of the Administrative Staff of Kermanshah University of Medical Sciences, Kermanshah, Iran, in 2016
Bibliographic record
Abstract
Workplace stress affects employees’ mental health, causing severe damage to their job functions and safety. Moreover, the quality of working life is among factors that play prominent roles in the workplace. The present experimental study aimed to investigate the relationship between the quality of working life and mental health of the administrative staff of Kermanshah University of Medical Sciences, Kermanshah, Iran, in 2016. In this descriptive and correlational study conducted on the administrative staff of Kermanshah University of Medical Sciences in 2016, the sample size was calculated through Kerjcie and Morgan sample size table (n=267), and the samples were selected by simple random sampling. For data collection, a demographic information questionnaire, the Walton’s quality of working life questionnaire and the Goldberg’s general health questionnaire (GHQ-28) were used. In addition, to analyze the collected data, the Pearson correlation coefficient and multiple stepwise regressions were utilized with the help of SPSS 23 software. The mean score of quality of working life was 3.40 out of 5.0, and that of mental health was 2.98 out of 4.0. There was a significant positive correlation between quality of work life and mental health (p<0.001, r=0.305). Of the dimensions of quality of working life, fair and adequate remunerations and safe and sanitary working environments had the most effects or could predict mental health to the greatest degrees, respectively. The results of the present demonstrated that fair remunerations, not necessarily financial pays, and safe and sanitary working environments influenced the employees’ mental health. So, it is recommended that managerial support, improved supervision, provision of training programs and improving the physical conditions of the workplace be placed top of the agenda towards this objective.
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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 | 0.002 | 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 | 0.001 |
| 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".